Smaller conv nets

- support predict()
- move discount to OptionsEnv
- fix RenderOptions

.gitignore

@@ -1,3 +1,8 @@
+*.pkl
+*.pt
+*.zip
+**/ray/*
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -119,6 +124,7 @@ venv.bak/
 
 # VS Code project settings
 .project
+.vscode
 
 # mkdocs documentation
 /site
@@ -140,6 +146,8 @@ expert_data/
 
 # Results
 experiments/results/
+output/
 
 # Dependencies
 InteractionSimulator/
+imitation/

README.md

@@ -24,8 +24,26 @@ Once the repository has been set up, you can process and save expert track demon
 ```
 python src/expert_data.py --loc [LOCNUM] --track [TRACKNUM]
 ```
+You can (and should) process all tracks at once at location 0 with:
+```
+python src/expert_data.py --all-tracks
+```
 
-You can then load the experts actions and observations using
+You can then train a default behavior cloning policy with the following. Be sure to check help for main.py for running options.
+```
+python src/main.py --train
+```
+You can run tensorboard by running the following and opening `localhost:6006` (or alternatively port-forwarding 6006 from the remote server)
+```
+tensorboard --logdir output/
+```
+You can then test the learned policy with the following, and see the animation file in `output/`:
+```
+python src/main.py --test
+```
+
+
+You can load the experts actions manually
 ```
 from src import expert_data
 observations, actions = expert_data.load_expert_data(loc = [LOCNUM], track = [TRACKNUM])

config/networks.json5

@@ -1,34 +1,42 @@
 {
-    ego_state: {
+    ego_encoder: {
         input_dim: 5, // number of state vars
-        hidden_n: 1,
+        hidden_n: 0,
         hidden_dim: 5,
         output_dim: 5
     },
     deepsets: {
-        input_dim: 5, // number of relative state vars for others
+        input_dim: 6, // number of relative state vars for others
         phi: {
-            hidden_n: 1,
+            hidden_n: 2,
             hidden_dim: 20,
         },
         latent_dim: 20,
         rho: {
-            hidden_n: 1,
+            hidden_n: 2,
             hidden_dim: 10,
         },
         output_dim: 10
     },
     path_encoder: {
         input_dim: 40, // 2 * path length for (x,y) coordinates
-        hidden_n: 2,
+        hidden_n: 0,
         hidden_dim: 20,
         output_dim: 10,
     },
     head: {
         input_dim: 0, // computed in policy constructor
-        hidden_n: 1,
+        hidden_n: 3,
         hidden_dim: 50,
         output_dim: 1, // number of outputs e.g. number of actions, or just one
         final_activation: 'sigmoid',
-    }
+    },
+    optim: {
+            optimizer: 'adam',
+            lr: 1e-3,
+            weight_decay: 0.1,
+    },
+    train_epochs: 200,
+    train_batch_size: 32,
+    loss: 'huber',
 }

config/value_dice.json5

@@ -0,0 +1,85 @@
+{
+    policy_net: {
+        ego_encoder: {
+            input_dim: 5, // number of state vars
+            hidden_n: 0,
+            hidden_dim: 5,
+            output_dim: 5
+        },
+        deepsets: {
+            input_dim: 6, // number of relative state vars for others
+            phi: {
+                hidden_n: 2,
+                hidden_dim: 20,
+            },
+            latent_dim: 20,
+            rho: {
+                hidden_n: 2,
+                hidden_dim: 10,
+            },
+            output_dim: 10
+        },
+        path_encoder: {
+            input_dim: 40, // 2 * path length for (x,y) coordinates
+            hidden_n: 0,
+            hidden_dim: 20,
+            output_dim: 10,
+        },
+        head: {
+            input_dim: 0, // computed in policy constructor
+            hidden_n: 3,
+            hidden_dim: 50,
+            output_dim: 1, // number of outputs e.g. number of actions, or just one
+            final_activation: 'sigmoid',
+        },
+    },
+    value_net: {
+        ego_encoder: {
+            input_dim: 5, // number of state vars
+            hidden_n: 0,
+            hidden_dim: 5,
+            output_dim: 5
+        },
+        deepsets: {
+            input_dim: 6, // number of relative state vars for others
+            phi: {
+                hidden_n: 2,
+                hidden_dim: 20,
+            },
+            latent_dim: 20,
+            rho: {
+                hidden_n: 2,
+                hidden_dim: 10,
+            },
+            output_dim: 10
+        },
+        path_encoder: {
+            input_dim: 40, // 2 * path length for (x,y) coordinates
+            hidden_n: 0,
+            hidden_dim: 20,
+            output_dim: 10,
+        },
+        action_dim: 1, // number of actions
+        head: {
+            input_dim: 0, // computed in policy constructor
+            hidden_n: 3,
+            hidden_dim: 50,
+            output_dim: 1, // number of outputs e.g. number of actions, or just one
+            final_activation: 'id',
+        },
+    },
+    policy_optim: {
+            optimizer: 'adam',
+            lr: 1e-3,
+            weight_decay: 0.1,
+    },
+    value_optim: {
+            optimizer: 'adam',
+            lr: 1e-3,
+            weight_decay: 0.1,
+    },
+    train_epochs: 200,
+    train_batch_size: 32,
+    discount: 0.95,
+    clip_grad_norm: 1.,
+}

experiments/experiment.py

@@ -0,0 +1,203 @@
+import json5
+from functools import partial
+import os
+opj = os.path.join
+
+# set up ray tune
+import ray
+from ray import tune
+from ray.tune import Analysis, ExperimentAnalysis
+from ray.tune.schedulers import ASHAScheduler
+from hyperopt import hp
+from ray.tune.suggest.hyperopt import HyperOptSearch
+
+# get graphs
+import intersim
+from intersim.graphs import ConeVisibilityGraph
+
+
+from src.main import basestr, main
+
+def parse_args():
+    """
+    Parse arguments to main
+    Returns:
+        kwargs: dictionary of arguments:
+            train (bool): whether to run train loop
+            test (bool): whether to run test loop
+            method (str): the method to try for imitation
+            loc (int): the location index of the roundabout
+            config (str): config path
+            seed (int): RNG seed
+    """
+    import argparse
+    parser = argparse.ArgumentParser(description='Save Expert Trajectories')
+    parser.add_argument('--loc', default=0, type=int,
+        help='location (default 0)')
+    parser.add_argument("--train", help="train model",
+        action="store_true")
+    parser.add_argument("--ray", help="use ray tune to run multiple experiments",
+        action="store_true")
+    parser.add_argument("--test", help="test model",
+        action="store_true") 
+    parser.add_argument("--method", help="modeling method",
+        choices=['bc', 'gail', 'advil', 'vd'], default='bc')
+    parser.add_argument("--config", help="config file path",
+        default=None, type=str)
+    parser.add_argument('--seed', default=0, type=int,
+        help='seed')    
+    parser.add_argument('--nframes', default=500, type=int,
+        help='frames for test animation') 
+    parser.add_argument('--nsamples', default=200, type=int,
+        help='number of ray samples') 
+    parser.add_argument('--graph', action='store_true',
+        help='whether to mask the relative states based on a ConeVisibilityGraph')
+    parser.add_argument('-d', default='./expert_data', type=str,
+                       help='data directory')
+    parser.add_argument('-o', default=None, type=str,
+                       help='output directory')                   
+    args = parser.parse_args()
+    kwargs = {
+        'train':args.train, 
+        'test':args.test, 
+        'method':args.method,
+        'loc':args.loc,
+        'config_path':args.config,
+        'seed':args.seed,
+        'ray':args.ray,
+        'nframes':args.nframes,
+        'nsamples':args.nsamples,
+        'datadir':os.path.abspath(args.d),
+        'graph':None,
+        'outdir': opj('output',args.method,'loc%02i'%(args.loc)),
+        'train_tracks':[0,1,2],
+        'cv_tracks':[3],
+        'test_tracks':[4],
+    }
+    if args.o:
+        kwargs['outdir'] = args.o
+    if args.graph:
+        kwargs['graph'] = ConeVisibilityGraph(r=20, half_angle=120)
+    return kwargs
+
+def get_full_config(ray_config:dict, method:str)->dict:
+    """
+    Get full model configuration from ray config and method string
+    Args:
+        ray_config (dict): ray config
+        method (str): method to get full configuration for
+    """
+    if method == 'bc':
+        from src.bc import bc_config
+        config = bc_config(ray_config)
+    elif method == 'vd':
+        from src.value_dice import vd_config
+        config = vd_config(ray_config)
+    else:
+        raise NotImplementedError
+    return config
+
+def get_ray_config(method:str)->dict:
+    """
+    Get configuration for ray based on method.
+    Args: 
+        method (str): method to get configuration for
+    Returns:
+        ray_config (dict): configuration for ray
+    """
+    if method == 'bc':
+        ray_config = {
+            "lr": tune.loguniform(1e-5, 1e-3),
+            "weight_decay": tune.choice([0, 0.1]),
+            "loss": tune.choice(['huber', 'mse']),
+            "train_batch_size": tune.choice([16,32,64]),
+            "deepsets_phi_hidden_n": tune.randint(1,5),
+            "deepsets_phi_hidden_dim": tune.lograndint(8,65),
+            "deepsets_latent_dim": tune.lograndint(8,129),
+            "deepsets_rho_hidden_n": tune.randint(0,3),
+            "deepsets_rho_hidden_dim": tune.lograndint(8,129),
+            "deepsets_output_dim": tune.lograndint(4,129),
+            "head_hidden_n": tune.randint(1,6),
+            "head_hidden_dim": tune.lograndint(16,257),
+            "head_final_activation": tune.choice(['sigmoid', None]),
+        }
+    elif method == 'vd':
+        ray_config = {
+            "policy_lr": tune.loguniform(1e-5, 1e-3),
+            "value_lr": tune.loguniform(1e-5, 1e-3),
+            "policy_weight_decay": tune.choice([0, 0.1]),
+            "value_weight_decay": tune.choice([0, 0.1]),
+            "train_batch_size": tune.choice([16,32,64]),
+            "deepsets_phi_hidden_n": tune.randint(1,5),
+            "deepsets_phi_hidden_dim": tune.lograndint(8,65),
+            "deepsets_latent_dim": tune.lograndint(8,129),
+            "deepsets_rho_hidden_n": tune.randint(0,3),
+            "deepsets_rho_hidden_dim": tune.lograndint(8,129),
+            "deepsets_output_dim": tune.lograndint(4,129),
+            "head_hidden_n": tune.randint(1,6),
+            "head_hidden_dim": tune.lograndint(16,257),
+            "head_final_activation": tune.choice(['sigmoid', None]),
+            "clip_grad_norm": tune.choice([.5, 1., 5., 10.]),
+            "discount": tune.choice([.95, .99])
+        }
+    else:
+        raise NotImplementedError
+    return ray_config
+
+if __name__ == '__main__':
+    kwargs = parse_args()
+    
+    # make prefix of output files
+
+    if kwargs['config_path']:
+        # load config
+        with open(kwargs['config_path'], 'r') as cfg:
+            config = json5.load(cfg)
+        if not os.path.isdir(kwargs['outdir']):
+            os.makedirs(kwargs['outdir'])   
+        filestr = opj(kwargs['outdir'], basestr(**kwargs)) 
+        if kwargs['ray']:
+            filestr = kwargs['config_path'].replace('_config.json','')
+        main(config, filestr=filestr, **kwargs)
+
+    elif kwargs['ray'] and kwargs['train']:
+
+        ray.shutdown() 
+        ray.init(log_to_driver=False)
+
+        def ray_train(config, datadir=None):
+            full_config = get_full_config(config, kwargs['method'])
+            main(full_config, filestr='exp', **kwargs)
+        
+        ray_config = get_ray_config(kwargs['method'])
+        search = HyperOptSearch(ray_config, max_concurrent=8, metric='cv_loss',mode="min",)
+        custom_scheduler = ASHAScheduler(metric='cv_loss', mode="min", grace_period=15)
+
+        analysis = tune.run(
+            ray_train, 
+            #config=ray_config,
+            search_alg=search,
+            scheduler=custom_scheduler,
+            local_dir=kwargs['outdir'],
+            #resources_per_trial={"cpu": 2},
+            time_budget_s=120*60,
+            num_samples=kwargs['nsamples'],
+        )
+    elif kwargs['ray'] and kwargs['test']:
+        analysis = Analysis(kwargs['outdir'], default_metric="cv_loss", default_mode="min")
+        config = analysis.get_best_config()
+        filepath = analysis.get_best_logdir()
+        filestr = opj(filepath, 'exp')
+        config_path = filestr+'_config.json'
+        with open(config_path, 'r') as cfg:
+            config = json5.load(cfg)
+        print("Best ray experiment:", filepath)
+        main(config, filestr=filestr, **kwargs)
+    else:
+        raise Exception('No valid config found')
+
+    
+    
+    
+
+    

experiments/experiments.sh

@@ -0,0 +1,9 @@
+#!/bin/sh
+
+python experiments/experiment.py --ray --train -d ./expert_data/base
+python experiments/experiment.py --ray --test -d ./expert_data/base --nframes 1000
+python experiments/experiment.py --ray --train -d ./expert_data/reg
+python experiments/experiment.py --ray --test -d ./expert_data/reg --nframes 1000
+python experiments/experiment.py --ray --train -d ./expert_data/reg_graph --graph
+python experiments/experiment.py --ray --test -d ./expert_data/reg_graph --graph --nframes 1000
+

experiments/train_vd.sh

@@ -0,0 +1,5 @@
+#!/bin/sh
+
+# python experiments/experiment.py --method vd --train --ray -d expert_data/reg -o output/vd/loc00/reg --nsamples 400
+# python experiments/experiment.py --test --ray --method vd -d expert_data/normal -o output/vd/loc00/normal --nframes 1000
+python experiments/experiment.py --train --method vd --config config/value_dice.json5

generate_demos.sh

@@ -0,0 +1,10 @@
+#DEFAULT PARAMETERS:
+# locs:list=None, (default to all locations)
+# tracks:list=None, (default to all tracks)
+# env_class:str='NRasterizedIncrementingAgent',
+# env_args:dict={width:36,height:36,m_per_px:2},
+# expert_class:str='NRasterizedRouteIncrementingAgent',  
+# expert_args:dict={mu:0.001}):
+
+# python -m src.data.expert --locs='[DR_USA_Roundabout_FT]' --tracks='[0]'
+python -m src.data.expert --locs='[DR_USA_Roundabout_FT]' --tracks='[0]'

requirements.txt

@@ -1,4 +1,10 @@
 tikzplotlib
 torch
+sklearn
 pytest
 json5
+tqdm
+ray[tune]
+hyperopt
+psutil
+fire

scratch/arec/intersimple/commands.txt

@@ -0,0 +1,22 @@
+python -m render_options --model_name='gail_options_image_mid_wcollision' --env='NRasterizedRoute' --options=True --width=36 --height=36 --m_per_px=2 --agent=50 --stop_on_collision=False
+
+import torch, os
+from src.data import load_experts
+folder = 'expert_data/DR_USA_Roundabout_FT/track0000'
+single_agent = os.path.join(folder, 'expert.pkl')
+multi_agent = os.path.join(folder,'joint_expert_states.pt')
+multi_agent_actions = os.path.join(folder,'joint_expert_actions.pt')
+demonstrations = load_experts([single_agent], flatten=False)
+demonstrations[0].__dict__.keys()
+len(demonstrations[0].obs)
+single_agent_lengths = [len(demonstration.obs) for demonstration in demonstrations]
+states = torch.load(multi_agent)
+actions = torch.load(multi_agent_actions)
+multi_agent_lengths = [sum(~torch.isnan(states[:,i,0])).item() for i in range(states.shape[1])]
+
+single_agent_actions = [demonstration.acts for demonstration in demonstrations]
+multi_agent_actions = [actions[~torch.isnan(actions[:,i,0])] for i in range(actions.shape[1])]
+
+import pickle
+with open(single_agent, "rb") as f:
+    new_trajectories = pickle.load(f)

scratch/arec/intersimple/data/expert.py

@@ -0,0 +1,145 @@
+from intersim.envs.intersimple import Intersimple
+from stable_baselines3.common.policies import BasePolicy
+import gym
+import intersim.envs.intersimple
+import imitation.data.rollout as rollout
+from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv
+from imitation.data.wrappers import RolloutInfoWrapper
+
+class IntersimExpert(BasePolicy):
+    
+    def __init__(self, intersim_env, mu=0, *args, **kwargs):
+        super().__init__(
+            observation_space=gym.spaces.Space(),
+            action_space=gym.spaces.Space(),
+            *args, **kwargs
+        )
+        self._intersim = intersim_env
+        self._mu = mu
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _action(self):
+        target_t = min(self._intersim._ind + 1, len(self._intersim._svt.simstate) - 1)
+        target_state = self._intersim._svt.simstate[target_t]
+        return self._intersim.target_state(target_state, mu=self._mu)
+
+    def predict(self, *args, **kwargs):
+        return self._action(), None
+
+class IntersimpleExpert(BasePolicy):
+
+    def __init__(self, intersimple_env, mu=0, *args, **kwargs):
+        super().__init__(
+            observation_space=intersimple_env.observation_space,
+            action_space=intersimple_env.action_space,
+            *args, **kwargs
+        )
+        self._intersimple = intersimple_env
+        self._intersim_expert = IntersimExpert(intersimple_env._env, mu=mu)
+    
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _action(self):
+        return self._intersim_expert._action()[self._intersimple._agent]
+
+    def predict(self, *args, **kwargs):
+        return self._action(), None
+
+class NormalizedIntersimpleExpert(IntersimpleExpert):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def predict(self, *args, **kwargs):
+        action, _ = super().predict(*args, **kwargs)
+        return self._intersimple._normalize(action), None
+
+class DummyVecEnvPolicy(BasePolicy):
+
+    def __init__(self, experts):
+        self._experts = [e() for e in experts]
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+    
+    def predict(self, *args, **kwargs):
+        predictions = [e.predict() for e in self._experts]
+        actions = [p[0] for p in predictions]
+        states = [p[1] for p in predictions]
+        return actions, states
+    
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+
+def save_video(env, expert):
+    env.reset()
+    env.render()
+    done = False
+    while not done:
+        actions, _ = expert.predict()
+        _, _, done, _ = env.step(actions)
+        env.render()
+    env.close()
+
+def demonstrations(expert='NormalizedIntersimpleExpert', env='NRasterizedIncrementingAgent', path=None, min_timesteps=None, min_episodes=None, video=False, env_args={}, policy_args={}):
+    """Rollout and save expert demos.
+    
+    Usage:
+        python -m intersimple.expert <flags>
+    Args: 
+        expert (class): class of expert
+        env (class): class of env intersim.envs.intersimple
+        path (str): path to store output
+        min_timesteps (int): min number of timesteps for call to rollout.rollout_and_save
+        min_episodes (int): min number of episodes for call to rollout.rollout_and_save
+        video (bool): whether to save a video of the expert until a single environment instantiation stops
+        env_args (dict): dictionary of kwargs when instantiating environment class
+        policy_args (dict): dictionary of kwargs when instantiating Expert policy
+    """
+    
+    Env = intersim.envs.intersimple.__dict__[env]
+    Expert = globals()[expert]
+
+    env = Env(**env_args)
+    info_env = RolloutInfoWrapper(env) # getting rollout info (dictionary) from environment
+    venv = DummyVecEnv([lambda: info_env]) # making a DummyVecEnv with a list of a function that when called returns the rollout info
+
+    policy = Expert(env, **policy_args) # instantiate an expert policy from specified class with instantiated environment and policy kwargs
+    venv_policy = DummyVecEnvPolicy([lambda: policy]) # make a DummyVecEnvPolicy with a list of a function that when called returns the Expert policy
+
+    if min_timesteps is None and min_episodes is None:
+        min_episodes = env.nv # one episode per vehicle being controlled in environment (hopefully an incrementing agent environment)
+
+    if video:
+        save_video(env, policy)
+    
+    path = path or (policy.__class__.__name__ + '_' + env.__class__.__name__ + '.pkl')
+    suntil = rollout.make_sample_until(
+            min_timesteps=min_timesteps,
+            min_episodes=min_episodes,
+        )
+    rollout.rollout_and_save(
+        path=path,
+        policy=venv_policy,
+        venv=venv,
+        sample_until=suntil
+    )
+
+if __name__ == '__main__':
+    import fire
+    fire.Fire(demonstrations)

scratch/arec/intersimple/data/generate.sh

@@ -0,0 +1,9 @@
+#python -m expert --env=IntersimpleReward --min_timesteps=200 --env_args='{agent:51}' --path='NormalizedIntersimpleExpert_IntersimpleRewardAgent51.pkl'
+#python -m expert --env=IntersimpleReward --min_timesteps=200 --env_args='{agent:51}' --policy_args='{mu:0.005}' --path='NormalizedIntersimpleExpert_IntersimpleRewardAgent51Mu.005.pkl'
+#python -m expert --env=IntersimpleReward --min_timesteps=200 --env_args='{agent:51}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpert_IntersimpleRewardAgent51Mu.001.pkl'
+#python -m expert --env=NRasterized --min_timesteps=200 --env_args='{agent:51,width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl'
+# python -m expert --env=NRasterizedRandomAgent --min_timesteps=10000 --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N10000_NRasterizedRandomAgentw36h36mppx2.pkl'
+#python -m expert --env=NRasterized --min_timesteps=200 --env_args='{agent:51,width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl'
+#python -m expert --env=NRasterized --min_timesteps=3000 --video --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedRandomAgentw36h36mppx2.pkl'
+#python -m expert --env=NRasterizedIncrementingAgent --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedIncrementingAgentw36h36mppx2.pkl'
+python -m process_all_experts --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}'

scratch/arec/intersimple/data/load_experts.py

@@ -0,0 +1,23 @@
+import pickle
+import imitation.data.rollout as rollout
+from tqdm import tqdm
+
+def load_experts(expert_files=[]):
+    """
+    Load expert trajectories from files and combine their transitions into a single RB
+
+    Args:
+        expert_files (list): list of expert file strings
+    Returns:
+        transitions (list): list of combined expert episode transitions 
+    """
+    transitions = []
+    for file in tqdm(expert_files):
+        with open(file, "rb") as f:
+            trajectories = pickle.load(f)
+        transitions = transitions + rollout.flatten_trajectories(trajectories)
+    return transitions
+
+if __name__=='__main__':
+    import fire
+    fire.Fire(load_experts)

scratch/arec/intersimple/data/process_all_experts.py

@@ -0,0 +1,48 @@
+import tqdm
+import expert
+import copy
+import os
+import intersim
+from tqdm import tqdm
+
+def process_all_experts(filename='expert.pkl',env_args={}, policy_args={}):
+    """
+    Process all experts in the Interaction Dataset
+    For now, using NormalizedIntersimpleExpert with NRasterizedIncrementingAgent environment
+
+    Args:
+        filename (str): name for track file
+        env_args (dict): default environment kwargs
+        policy_args (dict): default policy kwargs
+    """
+    I, J = len(intersim.LOCATIONS), intersim.MAX_TRACKS
+    pbar = tqdm(total=I*J)
+    for loc in range(I):
+        for track in range(J):
+            
+            it_env_args = copy.deepcopy(env_args)
+            it_env_args.update({
+                'loc':loc,
+                'track':track,
+            })
+            out_folder = os.path.join(intersim.LOCATIONS[loc], 'track%04i'%(track))
+            if not os.path.isdir(out_folder):
+                os.makedirs(out_folder) 
+            it_path = os.path.join(out_folder,filename)
+
+            expert.demonstrations(
+                expert='NormalizedIntersimpleExpert', 
+                env='NRasterizedIncrementingAgent', 
+                path=it_path, 
+                env_args=it_env_args, 
+                policy_args=policy_args,
+                )
+            pbar.update(1)
+    pbar.close()
+    
+
+if __name__=='__main__':
+    import fire
+    fire.Fire(process_all_experts)
+
+

scratch/arec/intersimple/gail/discriminator.py

@@ -0,0 +1,101 @@
+import torch
+
+# imitation.rewards.discrim_nets.DiscrimNetGAIL is composed of self.discriminator (nn.Module),
+# which gets called with inputs (state, action) when needed.
+
+class CnnDiscriminator(torch.nn.Module):
+    """ConvNet similar to stable_baselines3.common.policies.ActorCriticCnnPolicy."""
+
+    def __init__(self, env):
+        super().__init__()
+
+        obs_channels, _, _ = env.observation_space.shape
+        (action_size,) = env.action_space.shape
+        in_channels = obs_channels + action_size
+
+        self.cnn = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels, 32, kernel_size=(8, 8), stride=(4, 4)), # 5+1 -> 32
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2)), # 32 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)), # 64 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Flatten(start_dim=1, end_dim=-1),
+            torch.nn.LazyLinear(512), # 28224 -> 512
+            torch.nn.ReLU(),
+            torch.nn.LazyLinear(1), # 512 -> 1
+        )
+    
+    @staticmethod
+    def _concatenate(state, action):
+        b, _, h, w = state.shape
+        _, a = action.shape
+        act = action.unsqueeze(-1).unsqueeze(-1).expand((b, a, h, w))
+        sa = torch.cat((state, act), -3)
+        return sa
+
+    def forward(self, state, action):
+        sa = self._concatenate(state, action)
+        assert sa.ndim == 4
+        return self.cnn(sa).squeeze(1)
+
+class CnnDiscriminatorFlatAction(torch.nn.Module):
+    """ConvNet similar to stable_baselines3.common.policies.ActorCriticCnnPolicy."""
+
+    def __init__(self, env):
+        super().__init__()
+
+        obs_channels, _, _ = env.observation_space.shape
+        (action_size,) = env.action_space.shape
+        in_channels = obs_channels
+
+        self.cnn = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels, 32, kernel_size=(8, 8), stride=(4, 4)), # in_channels -> 32
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2)), # 32 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)), # 64 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Flatten(start_dim=1, end_dim=-1),
+            torch.nn.LazyLinear(128), # 28224 -> 128
+        )
+        self.decoder = torch.nn.Sequential(
+            torch.nn.LazyLinear(64), #128 + 2 -> 64
+            torch.nn.ReLU(),
+            torch.nn.LazyLinear(64), #64 -> 64
+            torch.nn.ReLU(),
+            torch.nn.LazyLinear(1) #64 -> 1
+        )
+    
+    @staticmethod
+    def _concatenate(state, action):
+        b, s= state.shape
+        b, a = action.shape
+        sa = torch.cat((state, action), -1)
+        return sa
+
+    def forward(self, state, action):
+        s = self.cnn(state.float())
+        sa = self._concatenate(s, action)
+        assert sa.ndim == 2
+        return self.decoder(sa).squeeze(1)
+
+class MlpDiscriminator(torch.nn.Module):
+    """MLP similar to stable_baselines3.common.policies.ActorCriticPolicy."""
+
+    def __init__(self, env=None):
+        super().__init__()
+        self.flatten = torch.nn.Flatten(start_dim=1, end_dim=-1)
+        self.mlp = torch.nn.Sequential(
+            torch.nn.LazyLinear(64), # 42 -> 64
+            torch.nn.Tanh(),
+            torch.nn.LazyLinear(64), # 64 -> 64
+            torch.nn.Tanh(),
+            torch.nn.LazyLinear(1),  # 64 -> 1
+        )
+    
+    def forward(self, state, action):
+        flat = self.flatten(state)
+        sa = torch.cat((action, flat), -1)
+        assert sa.ndim == 2
+        return self.mlp(sa).squeeze(1)

scratch/arec/intersimple/gail/test_discriminator.py

@@ -0,0 +1,45 @@
+from intersim.envs.intersimple import NRasterized
+from discriminator import CnnDiscriminator
+import torch
+
+def test_image_concatenation():
+    env = NRasterized()
+    disc = CnnDiscriminator(env)
+    s = torch.tensor(env.reset()).unsqueeze(0)
+    a = torch.tensor([[0.5]])
+    sa = disc._concatenate(s, a)
+
+    assert s.shape == (1, 5, 200, 200)
+    assert a.shape == (1, 1)
+    assert sa.shape == (1, 6, 200, 200)
+    assert torch.allclose(sa[:, :5], 1.0 * s)
+    assert (sa[:, 5] == a.unsqueeze(-1)).all()
+
+def test_image_concatenation3():
+    env = NRasterized()
+    disc = CnnDiscriminator(env)
+
+    s1 = env.reset()
+    a1 = 0.15
+    s2, _, _, _ = env.step(0.9)
+    a2 = 0.25
+    s3, _, _, _ = env.step(-0.9)
+    a3 = 0.35
+    
+    s = torch.stack([
+        torch.tensor(s1),
+        torch.tensor(s2),
+        torch.tensor(s3)
+    ], axis=0)
+    a = torch.tensor([
+        [a1],
+        [a2],
+        [a3],
+    ])
+    sa = disc._concatenate(s, a)
+
+    assert s.shape == (3, 5, 200, 200)
+    assert a.shape == (3, 1)
+    assert sa.shape == (3, 6, 200, 200)
+    assert torch.allclose(sa[:, :5], 1.0 * s)
+    assert (sa[:, 5] == a.unsqueeze(-1)).all()

scratch/arec/intersimple/gail_image_multiagent_nocollision.py

@@ -0,0 +1,70 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import NRasterized
+
+from gail.discriminator import CnnDiscriminatorFlatAction
+
+model_name = 'gail_image_multiagent_nocollision'
+
+# %%
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedRandomAgentw36h36mppx2.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(NRasterized, n_envs=2, env_kwargs={'stop_on_collision':False, 'width': 36, 'height': 36, 'm_per_px': 2})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train GAIL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "GAIL/")
+gail_trainer = adversarial.GAIL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    #n_disc_updates_per_round=2048,
+    discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+    gen_algo=sb3.PPO("CnnPolicy", venv, verbose=1, n_steps=1024),
+    allow_variable_horizon=True,
+)
+gail_trainer.train(total_timesteps=100000)
+gail_trainer.gen_algo.save(model_name)
+
+#del gail_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = NRasterized(stop_on_collision=False, width=36, height=36, m_per_px=2)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/arec/intersimple/gail_image_singleagent_nocollision.py

@@ -0,0 +1,70 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import NRasterized
+
+from gail.discriminator import CnnDiscriminator
+
+model_name = 'gail_image_singleagent_nocollision'
+
+# %%
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(NRasterized, n_envs=2, env_kwargs={'agent':51, 'stop_on_collision':False, 'width': 36, 'height': 36, 'm_per_px': 2})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train GAIL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "GAIL/")
+gail_trainer = adversarial.GAIL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    #n_disc_updates_per_round=2048,
+    discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+    gen_algo=sb3.PPO("CnnPolicy", venv, verbose=1, n_steps=1024),
+    allow_variable_horizon=True,
+)
+gail_trainer.train(total_timesteps=100000)
+gail_trainer.gen_algo.save(model_name)
+
+#del gail_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = NRasterized(agent=51, width=36, height=36, m_per_px=2, stop_on_collision=False)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/arec/intersimple/gail_options_image.py

@@ -0,0 +1,172 @@
+# %%
+import sys
+sys.path.append('../../../')
+from src.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
+from src.policies import OptionsCnnPolicy
+from src.util import render_env
+from src.data import load_experts
+from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
+from src.gail.train import train_discriminator, train_generator
+
+from imitation.algorithms import adversarial
+from imitation.util import logger
+import imitation.data.rollout as rollout
+
+import stable_baselines3
+from stable_baselines3.common.env_util import make_vec_env
+
+import torch
+import torch.utils.data
+import numpy as np
+import itertools
+import gym
+import pickle
+import tempfile
+import pathlib
+from tqdm import tqdm
+
+from intersim.envs.intersimple import NRasterized, NRasterizedRoute, NRasterizedRandomAgent, NRasterizedIncrementingAgent, NRasterizedRouteRandomAgent
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10]] # option 0 is safe fallback
+
+def flatten_transitions(transitions):
+    return {
+        'obs': np.stack(list(t['obs'] for t in transitions), axis=0),
+        'next_obs': np.stack(list(t['next_obs'] for t in transitions), axis=0),
+        'acts': np.stack(list(t['acts'] for t in transitions), axis=0),
+        'dones': np.stack(list(t['dones'] for t in transitions), axis=0),
+    }
+
+def train(expert_data, env_class=NRasterizedRouteRandomAgent, env_settings={}, 
+        epochs=10, discrim_batch_size=32, generator_steps=2048, discount=0.99):
+    """
+    Args: 
+        expert_data: list of transitions
+        env_class: environment class
+        env_settings: environment settings
+        epochs: number of epochs to train for
+        discrim_batch_size: discriminator batch size
+        generator_steps: number of steps taken in generator
+        discount: discount factor
+    Returns:
+        generator (stable_baselines3.PPO): options policy
+    """
+    env = env_class(**env_settings)
+    env.discount = discount
+
+    tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+    tempdir_path = pathlib.Path(tempdir.name)
+    logger.configure(tempdir_path / "GAIL/")
+    print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+    venv = make_vec_env(env_class, n_envs=1, env_kwargs=env_settings)
+    discriminator = adversarial.GAIL(
+        expert_data=expert_data,
+        expert_batch_size=discrim_batch_size,
+        discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+        #discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        venv=venv, # unused
+        gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+    )
+
+    generator = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env, options=ALL_OPTIONS),
+        verbose=1,
+        n_steps=generator_steps,
+    )
+
+    # PPO.train requires logger as set up in
+    # PPO._setup_learn (called by PPO.learn)
+    generator._logger = stable_baselines3.common.utils.configure_logger(
+        generator.verbose,
+        generator.tensorboard_log,
+    )
+
+    for _ in tqdm(range(epochs)):
+        train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=discrim_batch_size)
+        train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
+    
+    return generator
+
+# %%
+if __name__ == '__main__':
+    # %%
+    model_name = 'gail_options_image_mid_wcollision'
+    env_class = NRasterizedRouteRandomAgent
+    env_settings = {'width': 36, 'height': 36, 'm_per_px': 2, 'stop_on_collision': False}
+    
+    #env_class = NRasterized
+    #env_settings = {'agent': 51, 'width': 36, 'height': 36, 'm_per_px': 2}
+    files = ['../../../expert_data/DR_USA_Roundabout_FT/track%04i/expert.pkl'%(i) for i in range(5)]
+    transitions=load_experts(files)
+
+    generator = train(
+        transitions,
+        env_class=env_class,
+        env_settings=env_settings,
+        epochs=2, 
+        discrim_batch_size=256, 
+        generator_steps=10,#256, 
+        discount=0.99
+        )
+
+    generator.save(model_name)
+
+    # Render
+    render_settings = {'width': 36, 'height': 36, 'm_per_px': 2, 'agent':51, 'stop_on_collision': False}
+    render_env(model_name=model_name, env='NRasterizedRoute', options=True, options_list=ALL_OPTIONS,
+        **render_settings)
+
+
+# %% Tests
+
+def test_ll_expert_data():
+    with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
+        expert_trajectories = pickle.load(f)
+    expert_transitions = rollout.flatten_trajectories(expert_trajectories)
+
+    env = LLOptions(NRasterized(agent=51, width=36, height=36, m_per_px=2))
+
+    gen_transitions = list(itertools.islice(env.sample_ll(
+        policy=stable_baselines3.PPO(
+            OptionsCnnPolicy,
+            OptionsEnv(env),
+            verbose=1,
+        )
+    ), 10))
+    gen_transitions = flatten_transitions(gen_transitions)
+
+    assert expert_transitions[:10].obs.shape == gen_transitions['obs'].shape
+    assert expert_transitions[:10].next_obs.shape == gen_transitions['next_obs'].shape
+    assert expert_transitions[:10].acts.shape == gen_transitions['acts'].shape
+    assert expert_transitions[:10].dones.shape == gen_transitions['dones'].shape
+
+def test_ll_states():
+    env = NRasterized()
+    policy = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env),
+        verbose=1,
+    )
+    llenv = LLOptions(env)
+    transitions = list(itertools.islice(llenv.sample_ll(policy=policy), 100))
+
+    env2 = NRasterized()
+    s2 = env2.reset()
+    for i, t in enumerate(transitions):
+        assert i == 0 or np.array_equal(t['obs'], transitions[i-1]['next_obs'])
+        assert np.array_equal(t['obs'], s2)
+        assert t['acts'].shape == (1,)
+
+        nexts2, _, done2, _ = env2.step(t['acts'])
+        assert np.array_equal(t['next_obs'], nexts2)
+        assert np.array_equal(t['dones'], done2)
+
+        if done2:
+            break
+
+        s2 = nexts2
+
+def test_hl_transitions():
+    pass

scratch/arec/intersimple/gail_options_scratch.py

@@ -0,0 +1,559 @@
+# %%
+from gail.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
+from imitation.algorithms import adversarial
+import stable_baselines3
+import torch.utils.data
+import numpy as np
+from intersim.envs.intersimple import NRasterized, NRasterizedRandomAgent
+import itertools
+from torch.distributions import Categorical
+import gym
+import torch
+import pickle
+import imitation.data.rollout as rollout
+import tempfile
+import pathlib
+from imitation.util import logger
+from stable_baselines3.common.env_util import make_vec_env
+from tqdm import tqdm
+
+import logging 
+logging.basicConfig(level=logging.DEBUG)
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is safe fallback
+
+class OptionsCnnPolicy(stable_baselines3.common.policies.ActorCriticCnnPolicy):
+    """
+    Class for high-level options policy (generator)
+    """
+    def __init__(self, observation_space, *args, **kwargs):
+        super().__init__(observation_space['obs'], *args, **kwargs)
+
+    def _prior_distribution(self, s):
+        """
+        Return prior distribution over high-level options (before masking)
+        Args:
+            s (torch.tensor): observation
+        Returns:
+            values (torch.tensor): values from critic
+            dist (torch.distributions): prior distribution over actions
+        """
+        latent_pi, latent_vf, latent_sde = self._get_latent(s) 
+        distribution = self._get_action_dist_from_latent(latent_pi, latent_sde) 
+        values = self.value_net(latent_vf)
+        return values, distribution.distribution
+
+    def predict(self, obs):
+        """
+        Will mask invalid states before making action selections
+        Args:
+            obs: dict with keys:
+                obs (torch.tensor): (B,o)  true observations
+                mask (torch.tensor): (B,m) mask over valid actions
+        Returns:
+            ch (torch.tensor): (B,a) sampled actions
+            values (torch.tensor): (B,) predicted value at observation
+            log_probs (torch.tensor): (B,) log probabilities of selected actions
+        """
+        s, m = obs['obs'], obs['mask']
+        values, prior = self._prior_distribution(s)
+        posterior = Categorical(prior.probs * m)
+        ch = posterior.sample()
+        return ch, values, posterior.log_prob(ch)
+
+    def evaluate_actions(self, obs, ch):
+        """
+        Evaluate particular actions
+        Args:
+            obs: dict with keys:
+                obs (torch.tensor): (B,o) true observations
+                mask (torch.tensor): (B,m) masks over valid actions
+            ch (torch.tensor): (B,a) selected actions
+        Returns:
+            values (torch.tensor): (B,) predicted value at observation
+            log_probs (torch.tensor): (B,) log probabilities of selected actions
+            ent (torch.tensor): (B,) entropy of each distribution over actions
+        """
+        s, m = obs['obs'], obs['mask']
+        values, prior = self._prior_distribution(s)
+        posterior = Categorical(prior.probs * m)
+        return values, posterior.log_prob(ch), posterior.entropy() # additional values used by PPO.train
+
+class OptionsEnv(gym.Wrapper):
+    """
+    Wrap an intersimple environment with an options generator
+    """
+    def __init__(self, env, *args, **kwargs):
+        """
+        Initialize wrapped environment and set high-level action and observation spaces
+        """
+        super().__init__(env, *args, **kwargs)
+        num_hl_options = len(ALL_OPTIONS)
+        self.action_space = gym.spaces.Discrete(num_hl_options)
+        self.observation_space = gym.spaces.Dict({
+            'obs': env.observation_space,
+            'mask': gym.spaces.Box(low=0, high=1, shape=(num_hl_options,)),
+        })
+
+    def _after_choice(self):
+        pass
+    
+    def _after_step(self):
+        pass
+
+    def _transitions(self):
+        raise NotImplementedError('Use `LLOptions` or `HLOptions` for sampling.')
+    
+    def sample(self, generator):
+        """
+        yield transitions using a generator
+        Args:
+            generator (sb3.PPO)
+        Yields:
+
+        """
+        self.done = True
+        while True:
+            self.episode_start = False
+
+            if self.done:
+                # reset environment
+                self.s = self.env.reset()
+                self.m = available_actions(self.env)
+                self.done = False
+                self.episode_start = True
+
+            # set the action, the value of the start state, and the logprob of the action
+            # according to the current environment state and mask
+            self.ch, self.value, self.log_prob = generator.policy.predict({
+                'obs': torch.tensor(self.s).unsqueeze(0).to(generator.policy.device),
+                'mask': torch.tensor(self.m).unsqueeze(0).to(generator.policy.device),
+            })
+
+            # store a float list of actions to take given the option selected in the environment
+            self.plan = list(map(float, generate_plan(self.env, self.ch)))
+
+            # run whatever _after_choice might dictate in a child class
+            self._after_choice()
+
+            # some checks
+            assert not self.done
+            assert self.plan
+            assert feasible(self.env, self.plan, self.ch)
+
+            # execute the option so long as the episode isn't complete and the plan is still feasible
+            while not self.done and self.plan and feasible(self.env, self.plan, self.ch):
+                
+                # pop first action
+                self.a, self.plan = self.plan[0], self.plan[1:]
+                
+                # normalize action ??
+                self.a = self.env._normalize(self.a)
+                
+                # step through environment
+                self.nexts, _, self.done, _ = self.env.step(self.a)
+                self.nextm = available_actions(self.env)
+
+                # run whatever _after_step might dictate in child class
+                self._after_step()
+
+                # update state and mask to current
+                self.s = self.nexts
+                self.m = self.nextm
+
+            # transitions yielded from self._transitions() functions specied in child classes
+            yield from self._transitions()
+
+            ### NOTE: only yields after a full option has been executed / exited
+
+class LLOptions(OptionsEnv):
+    """Sample low-level (state, action) tuples for discriminator training."""
+    
+    def __init__(self, *args, **kwargs):
+        """
+        LLOption uses the true LL observations
+        """
+        super().__init__(*args, **kwargs)
+        # overwrite observation space to just output obs directly
+        self.observation_space = self.observation_space['obs'] 
+
+    def _after_choice(self):
+        """
+        After each option choice, initialize/reset the transition buffer
+        """
+        self._transition_buffer = []
+
+    def _after_step(self):
+        """
+        After each ll action, append s, s', a, done to transition buffer
+        """
+        self._transition_buffer.append({
+            'obs': self.s,
+            'next_obs': self.nexts,
+            'acts': np.array((self.a,)),
+            'dones': np.array(self.done),
+        })
+
+    def _transitions(self):
+        """
+        Yield from the transition buffer
+        """
+        yield from self._transition_buffer
+    
+    def sample_ll(self, policy):
+        """
+        Args:
+            policy
+        Returns:
+            gen: iterable which samples low-level transitions from the environment
+        """
+        return self.sample(policy)
+
+class HLOptions(OptionsEnv):
+    """Sample high-level (state, action, reward) tuples for generator training."""
+    
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def _after_choice(self):
+        """
+        After an option selection, initialize total reward and number of steps
+        """
+        self.r = 0
+        self.steps = 0
+
+    def _after_step(self):
+        """
+        After each low-level action, add the discounted discriminated reward score (given a discriminator)
+        """
+        self.r += self.discount**self.steps * self.discriminator.discrim_net.reward_train(
+            state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()),
+            action=torch.tensor([[self.a]]).to(self.discriminator.discrim_net.device()),
+            next_state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
+            done=torch.tensor(self.done).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
+        )
+        self.steps += 1
+    
+    def _transitions(self):
+        """
+        Yield a single dictionary per high-level selected action
+        Fields:
+            obs: high-level state and mask at selection
+            action: chosen high-level action
+            reward: accumulated option reward
+            episode_start: whether the action was chosen at the episode start
+            value: the value estimate from the starting state
+            log_prob: the log_prob of the selected action from the starting state
+            done: whether the episode has ended
+
+        """
+        yield {
+            'obs': {'obs': self.s, 'mask': self.m},
+            'action': self.ch,
+            'reward': self.r.detach(),
+            'episode_start': self.episode_start,
+            'value': self.value.detach(),
+            'log_prob': self.log_prob.detach(),
+            'done': self.done,
+        }
+    
+    def sample_hl(self, policy, discriminator):
+        """
+        Args:
+            policy
+            discriminator: function with which to score rewards
+        Returns:
+            gen: iterable which samples high-level transitions from the environment
+        """
+        self.discriminator = discriminator
+        return self.sample(policy)
+
+class RenderOptions(LLOptions):
+
+    def _after_step(self):
+        """
+        Render the environment after each low-level step
+        """
+        super()._after_step()
+        self.env.render()
+    
+    def close(self, *args, **kwargs):
+        """
+        On 'close', close the environment
+        """
+        self.env.close(*args, **kwargs)
+
+def available_actions(env):
+    """Return mask of available actions given current `env` state."""
+    valid = np.array([feasible(env, generate_plan(env, i), i) for i in range(len(ALL_OPTIONS))])
+    return valid
+
+def target_velocity_plan(current_v: float, target_v: float, t: int, dt: float):
+    """Smoothly target a velocity in a given number of steps"""
+    # for now, constant acceleration
+    a = (target_v - current_v)  / (t * dt)
+    return a*np.ones((t,))
+
+def generate_plan(env, i):
+    """Generate input profile for high-level action `i`."""
+    assert i < len(ALL_OPTIONS), "Invalid option index {i}"
+    target_v, t = ALL_OPTIONS[i]
+    current_v = env._env.state[env._agent, 1].item() # extract from env
+    plan = target_velocity_plan(current_v, target_v, t, env._env._dt)
+    assert len(plan) == t, "incorrect plan length"
+    return plan
+
+def check_future_collisions_fast(env, actions):
+    """Checks whether `env._agent` would collide with other agents assuming `actions` as input.
+    
+    Vehicles are (over-)approximated by single circles.
+
+    Args:
+        env (gym.Env): current environment state
+        actions (list of torch.Tensor): list of B (T, nv, adims) T-length action profiles
+    Returns:
+        feasible (torch.Tensor): tensor of shape (B,) indicating whether the respective action profiles are collision-free
+    """
+    B, (T, nv, _) = len(actions), actions[0].shape
+
+    states = torch.stack(env._env.propagate_action_profile(actions), axis=0)
+    assert states.shape == (B, T, nv, 5)
+
+    distance = ((states[:, :, :, :2] - states[:, :, env._agent:env._agent+1, :2])**2).sum(-1).sqrt()
+    distance = torch.where(distance.isnan(), np.inf*torch.ones_like(distance), distance) # only collide with spawned agents
+    distance[:, :, env._agent] = np.inf # cannot collide with itself
+    assert distance.shape == (B, T, nv)
+
+    radius = (env._env._lengths**2 + env._env._widths**2).sqrt() / 2
+    min_distance = radius[env._agent] + radius
+    min_distance = min_distance.unsqueeze(0).unsqueeze(0)
+    assert min_distance.shape == (1, 1, nv)
+
+    return (distance > min_distance).all(-1).all(-1)
+
+def check_future_collisions_circles(env, actions, n_circles:int=2):
+    """Checks whether `env._agent` would collide with other agents assuming `actions` as input.
+    
+    Vehicles are (over-)approximated by multiple circles.
+
+    Args:
+        env (gym.Env): current environment state
+        actions (list of torch.Tensor): list of B (T, nv, adims) T-length action profiles
+    Returns:
+        feasible (torch.Tensor): tensor of shape (B,) indicating whether the respective action profiles are collision-free
+    """
+    assert n_circles >= 2
+    B, (T, nv, _) = len(actions), actions[0].shape
+
+    states = torch.stack(env._env.propagate_action_profile(actions), axis=0)
+    assert states.shape == (B, T, nv, 5)
+    centers = states[:, :, :, :2]
+    psi = states[:, :, :, 3]
+    lon = torch.stack([psi.cos(), psi.sin()],dim=-1) # (B, T, nv, 2)
+
+    # offset between [-env._env.lengths+env._env.widths/2, env._env.lengths/2-env._env.widths/2]
+    back = (-env._env._lengths/2+env._env._widths/2).unsqueeze(-1) # (nv, 1)
+    length = (env._env._lengths-env._env._widths).unsqueeze(-1) # (nv, 1)
+    diff_d =  back + length*(torch.arange(n_circles)/(n_circles-1)).unsqueeze(0)  # (nv, n_circles) 
+    assert diff_d.shape == (nv, n_circles)
+    
+    offsets = diff_d[None, None, :, :, None] *  lon[:, :, :, None, :]
+    assert offsets.shape == (B, T, nv, n_circles, 2)
+
+    expanded_centers=centers.unsqueeze(-2) + offsets #(B, T, nv, n_circles, 2)
+    assert expanded_centers.shape == (B, T, nv, n_circles, 2)
+    agent_centers = expanded_centers[:,:,env._agent:env._agent+1,:,:] #(B, T, 1, n_circles, 2)
+    ds = expanded_centers.reshape((B, T, nv*n_circles, 1, 2)) - agent_centers #(B, T, nv*nc,1, 2) - (B, T, 1, nc, 2) = (B, T, nv*nc, nc, 2) 
+
+    distance = (ds**2).sum(-1).sqrt().reshape((B, T, nv, n_circles, n_circles)) # (B, T, nv, nc, nc)
+    distance = torch.where(distance.isnan(), np.inf*torch.ones_like(distance), distance) # only collide with spawned agents
+    distance[:, :, env._agent] = np.inf # cannot collide with itself
+    assert distance.shape == (B, T, nv, n_circles, n_circles)
+
+    radius = env._env._widths*np.sqrt(2) / 2
+    min_distance = radius[env._agent] + radius
+    min_distance = min_distance[None, None, :, None, None]
+    assert min_distance.shape == (1, 1, nv, 1, 1)
+
+    return (distance > min_distance).all(-1).all(-1).all(-1).all(-1)
+
+def feasible(env, plan, ch):
+    """Check if input profile is feasible given current `env` state. Action `ch=0` is safe fallback."""
+    
+    # zero pad plan - Take (T,) np plan and convert it to (T, nv, 1) torch.Tensor
+    full_plan = torch.zeros(len(plan), env._env._nv, 1)
+    full_plan[:, env._agent, 0] = torch.tensor(plan)
+    # valid = check_future_collisions_fast(env, [full_plan]) # check_future_collisions_fast takes in B-list and outputs (B,) bool tensor
+    valid = check_future_collisions_circles(env, [full_plan])
+    return ch == 0 or valid.item()
+
+def flatten_transitions(transitions):
+    return {
+        'obs': np.stack(list(t['obs'] for t in transitions), axis=0),
+        'next_obs': np.stack(list(t['next_obs'] for t in transitions), axis=0),
+        'acts': np.stack(list(t['acts'] for t in transitions), axis=0),
+        'dones': np.stack(list(t['dones'] for t in transitions), axis=0),
+    }
+
+def train_discriminator(env, generator, discriminator, num_samples):
+    transitions = list(itertools.islice(env.sample_ll(generator), num_samples))
+    generator_samples = flatten_transitions(transitions)
+    discriminator.train_disc(gen_samples=generator_samples)
+
+def train_generator(env, generator, discriminator, num_samples):
+    generator_samples = list(itertools.islice(env.sample_hl(generator, discriminator), num_samples+1))
+    
+    generator.rollout_buffer.reset()
+    for s in generator_samples[:-1]:
+        generator.rollout_buffer.add(
+            obs=s['obs'],
+            action=s['action'].cpu(),
+            reward=s['reward'].cpu(),
+            episode_start=s['episode_start'],
+            value=s['value'],
+            log_prob=s['log_prob'],
+        )
+    
+    generator.rollout_buffer.compute_returns_and_advantage(
+        last_values=generator_samples[-1]['value'],
+        dones=generator_samples[-1]['done'],
+    )
+    
+    generator.train()
+
+def train(expert_data, env_class=NRasterizedRandomAgent, env_settings={}, epochs=10, discrim_batch_size=32, generator_steps=2048, discount=0.99):
+    """
+    Args: 
+        expert_data: list of transitions
+        env_class: environment class
+        env_settings: environment settings
+        epochs: number of epochs to train for
+        discrim_batch_size: discriminator batch size
+        generator_steps: number of steps taken in generator
+        discount: discount factor
+    Returns:
+        generator (stable_baselines3.PPO): options policy
+    """
+    env = env_class(**env_settings)
+    env.discount = discount
+
+    tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+    tempdir_path = pathlib.Path(tempdir.name)
+    logger.configure(tempdir_path / "GAIL/")
+    print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+    venv = make_vec_env(env_class, n_envs=1, env_kwargs=env_settings)
+    discriminator = adversarial.GAIL(
+        expert_data=expert_data,
+        expert_batch_size=discrim_batch_size,
+        discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+        #discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        venv=venv, # unused
+        gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+    )
+
+    generator = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env),
+        verbose=1,
+        n_steps=generator_steps,
+    )
+
+    # PPO.train requires logger as set up in
+    # PPO._setup_learn (called by PPO.learn)
+    generator._logger = stable_baselines3.common.utils.configure_logger(
+        generator.verbose,
+        generator.tensorboard_log,
+    )
+
+    for _ in tqdm(range(epochs)):
+        train_discriminator(LLOptions(env), generator, discriminator, num_samples=discrim_batch_size)
+        train_generator(HLOptions(env), generator, discriminator, num_samples=generator_steps)
+    
+    return generator
+
+# %%
+if __name__ == '__main__':
+    # %%
+    model_name = 'gail_options_image'
+    env_class = NRasterizedRandomAgent
+    env_settings = {'width': 36, 'height': 36, 'm_per_px': 2}
+
+    with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedIncrementingAgentw36h36mppx2.pkl", "rb") as f:
+        trajectories = pickle.load(f)
+    #import pdb 
+    #pdb.set_trace()
+    transitions = rollout.flatten_trajectories(trajectories)
+    generator = train(
+        transitions,
+        env_class=env_class,
+        env_settings=env_settings,
+        epochs=2, 
+        discrim_batch_size=32, 
+        generator_steps=2048, 
+        discount=0.99
+        )
+
+    generator.save(model_name) # save ppo sb3 generator class
+
+    # %%
+    model = stable_baselines3.PPO.load(model_name) # not actually used
+
+    env = RenderOptions(NRasterizedRandomAgent(**env_settings))
+    for s in env.sample_ll(generator):
+        if s['dones']:
+            break
+
+    env.close(filestr='render/'+model_name)
+
+# %% Tests
+
+def test_ll_expert_data():
+    with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
+        expert_trajectories = pickle.load(f)
+    expert_transitions = rollout.flatten_trajectories(expert_trajectories)
+
+    env = LLOptions(NRasterized(agent=51, width=36, height=36, m_per_px=2))
+
+    gen_transitions = list(itertools.islice(env.sample_ll(
+        policy=stable_baselines3.PPO(
+            OptionsCnnPolicy,
+            OptionsEnv(env),
+            verbose=1,
+        )
+    ), 10))
+    gen_transitions = flatten_transitions(gen_transitions)
+
+    assert expert_transitions[:10].obs.shape == gen_transitions['obs'].shape
+    assert expert_transitions[:10].next_obs.shape == gen_transitions['next_obs'].shape
+    assert expert_transitions[:10].acts.shape == gen_transitions['acts'].shape
+    assert expert_transitions[:10].dones.shape == gen_transitions['dones'].shape
+
+def test_ll_states():
+    env = NRasterized()
+    policy = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env),
+        verbose=1,
+    )
+    llenv = LLOptions(env)
+    transitions = list(itertools.islice(llenv.sample_ll(policy=policy), 100))
+
+    env2 = NRasterized()
+    s2 = env2.reset()
+    for i, t in enumerate(transitions):
+        assert i == 0 or np.array_equal(t['obs'], transitions[i-1]['next_obs'])
+        assert np.array_equal(t['obs'], s2)
+        assert t['acts'].shape == (1,)
+
+        nexts2, _, done2, _ = env2.step(t['acts'])
+        assert np.array_equal(t['next_obs'], nexts2)
+        assert np.array_equal(t['dones'], done2)
+
+        if done2:
+            break
+
+        s2 = nexts2
+
+def test_hl_transitions():
+    pass

scratch/arec/intersimple/options_gail.py

@@ -0,0 +1,510 @@
+# %%
+from gail.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
+from imitation.algorithms import adversarial
+import stable_baselines3
+import torch.utils.data
+import numpy as np
+from intersim.envs.intersimple import NRasterized, NRasterizedRandomAgent
+import itertools
+from torch.distributions import Categorical
+import gym
+import torch
+import pickle
+import imitation.data.rollout as rollout
+import tempfile
+import pathlib
+from imitation.util import logger
+from stable_baselines3.common.env_util import make_vec_env
+from tqdm import tqdm
+
+import logging 
+logging.basicConfig(level=logging.DEBUG)
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is safe fallback
+
+class OptionsCnnPolicy(stable_baselines3.common.policies.ActorCriticCnnPolicy):
+    """
+    Class for high-level options policy (generator)
+    """
+    def __init__(self, observation_space, *args, **kwargs):
+        super().__init__(observation_space['obs'], *args, **kwargs)
+
+    def _prior_distribution(self, s):
+        """
+        Return prior distribution over high-level options (before masking)
+        Args:
+            s (torch.tensor): observation
+        Returns:
+            values (torch.tensor): values from critic
+            dist (torch.distributions): prior distribution over actions
+        """
+        latent_pi, latent_vf, latent_sde = self._get_latent(s) 
+        distribution = self._get_action_dist_from_latent(latent_pi, latent_sde) 
+        values = self.value_net(latent_vf)
+        return values, distribution.distribution
+
+    def predict(self, obs):
+        """
+        Will mask invalid states before making action selections
+        Args:
+            obs: dict with keys:
+                obs (torch.tensor): (B,o)  true observations
+                mask (torch.tensor): (B,m) mask over valid actions
+        Returns:
+            ch (torch.tensor): (B,a) sampled actions
+            values (torch.tensor): (B,) predicted value at observation
+            log_probs (torch.tensor): (B,) log probabilities of selected actions
+        """
+        s, m = obs['obs'], obs['mask']
+        values, prior = self._prior_distribution(s)
+        posterior = Categorical(prior.probs * m)
+        ch = posterior.sample()
+        return ch, values, posterior.log_prob(ch)
+
+    def evaluate_actions(self, obs, ch):
+        """
+        Evaluate particular actions
+        Args:
+            obs: dict with keys:
+                obs (torch.tensor): (B,o) true observations
+                mask (torch.tensor): (B,m) masks over valid actions
+            ch (torch.tensor): (B,a) selected actions
+        Returns:
+            values (torch.tensor): (B,) predicted value at observation
+            log_probs (torch.tensor): (B,) log probabilities of selected actions
+            ent (torch.tensor): (B,) entropy of each distribution over actions
+        """
+        s, m = obs['obs'], obs['mask']
+        values, prior = self._prior_distribution(s)
+        posterior = Categorical(prior.probs * m)
+        return values, posterior.log_prob(ch), posterior.entropy() # additional values used by PPO.train
+
+class OptionsEnv(gym.Wrapper):
+    """
+    Wrap an intersimple environment with an options generator
+    """
+    def __init__(self, env, render=False, *args, **kwargs):
+        """
+        Initialize wrapped environment and set high-level action and observation spaces
+        """
+        super().__init__(env, *args, **kwargs)
+        num_hl_options = len(ALL_OPTIONS)
+        self.action_space = gym.spaces.Discrete(num_hl_options)
+        self.observation_space = gym.spaces.Dict({
+            'obs': env.observation_space,
+            'mask': gym.spaces.Box(low=0, high=1, shape=(num_hl_options,)),
+        })
+        self._hl_transition_buffer = []
+        self._ll_transition_buffer = []
+        self.render=render
+
+    def _after_option_choice(self):
+        """
+        After initial option choice, 
+        """
+        self._hl_r = 0
+        self._hl_steps = 0
+    
+    def _after_step(self):
+        """
+        After each step, add the ll transition to the appropriate buffer, add to reward, add to steps, and possibly render
+        """
+
+        self._ll_transition_buffer.append({
+            'obs': self.s,
+            'next_obs': self.nexts,
+            'acts': np.array((self.a,)),
+            'dones': np.array(self.done),
+        })  
+        self.r += self.discount**self.steps * self.discriminator.discrim_net.reward_train(
+            state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()),
+            action=torch.tensor([[self.a]]).to(self.discriminator.discrim_net.device()),
+            next_state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
+            done=torch.tensor(self.done).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
+        )
+        self.steps += 1
+        if self.render:
+            self.env.render()
+    
+    def _after_option(self):
+        """
+        After each low-level action, add the discounted discriminated reward score (given a discriminator)
+        """
+        self._hl_transition_buffer.append({
+            'obs': {'obs': self.os, 'mask': self.m},
+            'action': self.ch,
+            'reward': self.r.detach(),
+            'episode_start': self.episode_start,
+            'value': self.value.detach(),
+            'log_prob': self.log_prob.detach(),
+            'done': self.done,
+            })
+
+    def close(self, *args, **kwargs):
+        """
+        On 'close', close the environment
+        """
+        self.env.close(*args, **kwargs)
+    
+    def sample(self, generator, controller):
+        """
+        yield transitions using a generator
+        Args:
+            generator (sb3.PPO)
+            controller (str): 'high' or 'low' to yield from proper buffer
+        Yields:
+
+        """
+        self.done = True
+        # DO I WANT TO EMPTY THE BUFFERS??? Probs naw
+        while True:
+            
+            # yield from buffers to empty what was stored previously
+            if controller = 'high':
+                yield from self._hl_transition_buffer
+            elif controller == 'low':
+                yield from self._ll_transition_buffer
+            else:
+                raise('Improper buffer')
+            
+            self.episode_start = False
+            if self.done:
+                # reset environment
+                self.s = self.env.reset()
+                self.done = False
+                self.episode_start = True
+
+            self.os = self.s.copy() # option start state
+            self.m = available_actions(self.env)
+
+            # set the action, the value of the start state, and the logprob of the action
+            # according to the current environment state and mask
+            self.ch, self.value, self.log_prob = generator.policy.predict({
+                'obs': torch.tensor(self.os).unsqueeze(0).to(generator.policy.device),
+                'mask': torch.tensor(self.m).unsqueeze(0).to(generator.policy.device),
+            })
+
+            # store a float list of actions to take given the option selected in the environment
+            self.plan = list(map(float, generate_plan(self.env, self.ch)))
+
+            # run whatever _after_choice might dictate in a child class
+            self._after_option_choice()
+
+            # some checks
+            assert not self.done
+            assert self.plan
+            assert feasible(self.env, self.plan, self.ch)
+
+            # execute the option so long as the episode isn't complete and the plan is still feasible
+            while not self.done and self.plan and feasible(self.env, self.plan, self.ch):
+                
+                # pop first action
+                self.a, self.plan = self.plan[0], self.plan[1:]
+                
+                # normalize action ??
+                self.a = self.env._normalize(self.a)
+                
+                # step through environment
+                self.nexts, _, self.done, _ = self.env.step(self.a)
+
+                # run whatever _after_step might dictate in child class
+                self._after_step()
+
+                # update state and mask to current
+                self.s = self.nexts
+            
+            # run whatever to do after option
+            self._after_option()
+
+    def sample_ll(self, policy):
+        """
+        Not quite sure how this works????
+        Why would you do this over LLOptions.sample(policy)
+        """
+        return self.sample(policy, 'low')
+
+    def sample_hl(self, policy, discriminator):
+        """
+        Args:
+            policy
+            discriminator: function with which to score rewards
+        Returns:
+            gen: an which samples high-level transitions from the environment
+        """
+        self.discriminator = discriminator
+        return self.sample(policy)
+
+def available_actions(env):
+    """Return mask of available actions given current `env` state."""
+    valid = np.array([feasible(env, generate_plan(env, i), i) for i in range(len(ALL_OPTIONS))])
+    return valid
+
+def target_velocity_plan(current_v: float, target_v: float, t: int, dt: float):
+    """Smoothly target a velocity in a given number of steps"""
+    # for now, constant acceleration
+    a = (target_v - current_v)  / (t * dt)
+    return a*np.ones((t,))
+
+def generate_plan(env, i):
+    """Generate input profile for high-level action `i`."""
+    assert i < len(ALL_OPTIONS), "Invalid option index {i}"
+    target_v, t = ALL_OPTIONS[i]
+    current_v = env._env.state[env._agent, 1].item() # extract from env
+    plan = target_velocity_plan(current_v, target_v, t, env._env._dt)
+    assert len(plan) == t, "incorrect plan length"
+    return plan
+
+def check_future_collisions_fast(env, actions):
+    """Checks whether `env._agent` would collide with other agents assuming `actions` as input.
+    
+    Vehicles are (over-)approximated by single circles.
+
+    Args:
+        env (gym.Env): current environment state
+        actions (list of torch.Tensor): list of B (T, nv, adims) T-length action profiles
+    Returns:
+        feasible (torch.Tensor): tensor of shape (B,) indicating whether the respective action profiles are collision-free
+    """
+    B, (T, nv, _) = len(actions), actions[0].shape
+
+    states = torch.stack(env._env.propagate_action_profile(actions), axis=0)
+    assert states.shape == (B, T, nv, 5)
+
+    distance = ((states[:, :, :, :2] - states[:, :, env._agent:env._agent+1, :2])**2).sum(-1).sqrt()
+    distance = torch.where(distance.isnan(), np.inf*torch.ones_like(distance), distance) # only collide with spawned agents
+    distance[:, :, env._agent] = np.inf # cannot collide with itself
+    assert distance.shape == (B, T, nv)
+
+    radius = (env._env._lengths**2 + env._env._widths**2).sqrt() / 2
+    min_distance = radius[env._agent] + radius
+    min_distance = min_distance.unsqueeze(0).unsqueeze(0)
+    assert min_distance.shape == (1, 1, nv)
+
+    return (distance > min_distance).all(-1).all(-1)
+
+def check_future_collisions_circles(env, actions, n_circles:int=2):
+    """Checks whether `env._agent` would collide with other agents assuming `actions` as input.
+    
+    Vehicles are (over-)approximated by multiple circles.
+
+    Args:
+        env (gym.Env): current environment state
+        actions (list of torch.Tensor): list of B (T, nv, adims) T-length action profiles
+    Returns:
+        feasible (torch.Tensor): tensor of shape (B,) indicating whether the respective action profiles are collision-free
+    """
+    assert n_circles >= 2
+    B, (T, nv, _) = len(actions), actions[0].shape
+
+    states = torch.stack(env._env.propagate_action_profile(actions), axis=0)
+    assert states.shape == (B, T, nv, 5)
+    centers = states[:, :, :, :2]
+    psi = states[:, :, :, 3]
+    lon = torch.stack([psi.cos(), psi.sin()],dim=-1) # (B, T, nv, 2)
+
+    # offset between [-env._env.lengths+env._env.widths/2, env._env.lengths/2-env._env.widths/2]
+    back = (-env._env._lengths/2+env._env._widths/2).unsqueeze(-1) # (nv, 1)
+    length = (env._env._lengths-env._env._widths).unsqueeze(-1) # (nv, 1)
+    diff_d =  back + length*(torch.arange(n_circles)/(n_circles-1)).unsqueeze(0)  # (nv, n_circles) 
+    assert diff_d.shape == (nv, n_circles)
+    
+    offsets = diff_d[None, None, :, :, None] *  lon[:, :, :, None, :]
+    assert offsets.shape == (B, T, nv, n_circles, 2)
+
+    expanded_centers=centers.unsqueeze(-2) + offsets #(B, T, nv, n_circles, 2)
+    assert expanded_centers.shape == (B, T, nv, n_circles, 2)
+    agent_centers = expanded_centers[:,:,env._agent:env._agent+1,:,:] #(B, T, 1, n_circles, 2)
+    ds = expanded_centers.reshape((B, T, nv*n_circles, 1, 2)) - agent_centers #(B, T, nv*nc,1, 2) - (B, T, 1, nc, 2) = (B, T, nv*nc, nc, 2) 
+
+    distance = (ds**2).sum(-1).sqrt().reshape((B, T, nv, n_circles, n_circles)) # (B, T, nv, nc, nc)
+    distance = torch.where(distance.isnan(), np.inf*torch.ones_like(distance), distance) # only collide with spawned agents
+    distance[:, :, env._agent] = np.inf # cannot collide with itself
+    assert distance.shape == (B, T, nv, n_circles, n_circles)
+
+    radius = env._env._widths*np.sqrt(2) / 2
+    min_distance = radius[env._agent] + radius
+    min_distance = min_distance[None, None, :, None, None]
+    assert min_distance.shape == (1, 1, nv, 1, 1)
+
+    return (distance > min_distance).all(-1).all(-1).all(-1).all(-1)
+
+def feasible(env, plan, ch):
+    """Check if input profile is feasible given current `env` state. Action `ch=0` is safe fallback."""
+    
+    # zero pad plan - Take (T,) np plan and convert it to (T, nv, 1) torch.Tensor
+    full_plan = torch.zeros(len(plan), env._env._nv, 1)
+    full_plan[:, env._agent, 0] = torch.tensor(plan)
+    # valid = check_future_collisions_fast(env, [full_plan]) # check_future_collisions_fast takes in B-list and outputs (B,) bool tensor
+    valid = check_future_collisions_circles(env, [full_plan])
+    return ch == 0 or valid.item()
+
+def flatten_transitions(transitions):
+    return {
+        'obs': np.stack(list(t['obs'] for t in transitions), axis=0),
+        'next_obs': np.stack(list(t['next_obs'] for t in transitions), axis=0),
+        'acts': np.stack(list(t['acts'] for t in transitions), axis=0),
+        'dones': np.stack(list(t['dones'] for t in transitions), axis=0),
+    }
+
+def train_discriminator(env, generator, discriminator, num_samples):
+    transitions = list(itertools.islice(env.sample_ll(generator), num_samples))
+    generator_samples = flatten_transitions(transitions)
+    discriminator.train_disc(gen_samples=generator_samples)
+
+def train_generator(env, generator, discriminator, num_samples):
+    generator_samples = list(itertools.islice(env.sample_hl(generator, discriminator), num_samples+1))
+    
+    generator.rollout_buffer.reset()
+    for s in generator_samples[:-1]:
+        generator.rollout_buffer.add(
+            obs=s['obs'],
+            action=s['action'].cpu(),
+            reward=s['reward'].cpu(),
+            episode_start=s['episode_start'],
+            value=s['value'],
+            log_prob=s['log_prob'],
+        )
+    
+    generator.rollout_buffer.compute_returns_and_advantage(
+        last_values=generator_samples[-1]['value'],
+        dones=generator_samples[-1]['done'],
+    )
+    
+    generator.train()
+
+def train(expert_data, env_class=NRasterizedRandomAgent, env_settings={}, epochs=10, discrim_batch_size=32, generator_steps=2048, discount=0.99):
+    """
+    Args: 
+        expert_data: list of transitions
+        env_class: environment class
+        env_settings: environment settings
+        epochs: number of epochs to train for
+        discrim_batch_size: discriminator batch size
+        generator_steps: number of steps taken in generator
+        discount: discount factor
+    Returns:
+        generator (stable_baselines3.PPO): options policy
+    """
+    env = env_class(**env_settings)
+    env.discount = discount
+
+    tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+    tempdir_path = pathlib.Path(tempdir.name)
+    logger.configure(tempdir_path / "GAIL/")
+    print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+    venv = make_vec_env(env_class, n_envs=1, env_kwargs=env_settings)
+    discriminator = adversarial.GAIL(
+        expert_data=expert_data,
+        expert_batch_size=discrim_batch_size,
+        discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+        #discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        venv=venv, # unused
+        gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+    )
+
+    generator = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env),
+        verbose=1,
+        n_steps=generator_steps,
+    )
+
+    # PPO.train requires logger as set up in
+    # PPO._setup_learn (called by PPO.learn)
+    generator._logger = stable_baselines3.common.utils.configure_logger(
+        generator.verbose,
+        generator.tensorboard_log,
+    )
+
+    for _ in tqdm(range(epochs)):
+        train_discriminator(LLOptions(env), generator, discriminator, num_samples=discrim_batch_size)
+        train_generator(HLOptions(env), generator, discriminator, num_samples=generator_steps)
+    
+    return generator
+
+# %%
+if __name__ == '__main__':
+    # %%
+    model_name = 'gail_options_image'
+    env_class = NRasterizedRandomAgent
+    env_settings = {'width': 36, 'height': 36, 'm_per_px': 2}
+
+    with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedIncrementingAgentw36h36mppx2.pkl", "rb") as f:
+        trajectories = pickle.load(f)
+    #import pdb 
+    #pdb.set_trace()
+    transitions = rollout.flatten_trajectories(trajectories)
+    generator = train(
+        transitions,
+        env_class=env_class,
+        env_settings=env_settings,
+        epochs=2, 
+        discrim_batch_size=32, 
+        generator_steps=2048, 
+        discount=0.99
+        )
+
+    generator.save(model_name) # save ppo sb3 generator class
+
+    # %%
+    model = stable_baselines3.PPO.load(model_name) # not actually used
+
+    env = OptionsGail(NRasterizedRandomAgent(**env_settings), render=True)
+    for s in env.sample_ll(generator):
+        if s['dones']:
+            break
+
+    env.close(filestr='render/'+model_name)
+
+# %% Tests
+
+def test_ll_expert_data():
+    with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
+        expert_trajectories = pickle.load(f)
+    expert_transitions = rollout.flatten_trajectories(expert_trajectories)
+
+    env = LLOptions(NRasterized(agent=51, width=36, height=36, m_per_px=2))
+
+    gen_transitions = list(itertools.islice(env.sample_ll(
+        policy=stable_baselines3.PPO(
+            OptionsCnnPolicy,
+            OptionsEnv(env),
+            verbose=1,
+        )
+    ), 10))
+    gen_transitions = flatten_transitions(gen_transitions)
+
+    assert expert_transitions[:10].obs.shape == gen_transitions['obs'].shape
+    assert expert_transitions[:10].next_obs.shape == gen_transitions['next_obs'].shape
+    assert expert_transitions[:10].acts.shape == gen_transitions['acts'].shape
+    assert expert_transitions[:10].dones.shape == gen_transitions['dones'].shape
+
+def test_ll_states():
+    env = NRasterized()
+    policy = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env),
+        verbose=1,
+    )
+    llenv = LLOptions(env)
+    transitions = list(itertools.islice(llenv.sample_ll(policy=policy), 100))
+
+    env2 = NRasterized()
+    s2 = env2.reset()
+    for i, t in enumerate(transitions):
+        assert i == 0 or np.array_equal(t['obs'], transitions[i-1]['next_obs'])
+        assert np.array_equal(t['obs'], s2)
+        assert t['acts'].shape == (1,)
+
+        nexts2, _, done2, _ = env2.step(t['acts'])
+        assert np.array_equal(t['next_obs'], nexts2)
+        assert np.array_equal(t['dones'], done2)
+
+        if done2:
+            break
+
+        s2 = nexts2
+
+def test_hl_transitions():
+    pass

scratch/arec/intersimple/plan.txt

@@ -0,0 +1,55 @@
+Environment 
+-- each 'environment' follows a single roundabout and track id (recording of that roundabout)
+-- on reset, the environment we will use changes the vehicle to control while having the other agents follow their true data (expert controller)
+---- Note this can be problematic as it can lead to vehicles behind you crashing into you
+
+TRAINING
+---------
+1. Load pre-trained massive set of transitions
+-- For all roundabouts
+    -- For all tracks
+        -- For all vehicles
+            -- For all valid timesteps
+                -- Rasterized state (incl. path), action
+
+2. HGAIL
+-- For each epoch
+    -- INSTANTIATE A NEW ENVIRONMENT (Roundabout + Track) w/ randomized agent, from set of all expert environments
+        -- Train discriminator off training data + yielded low-level transitions in replay buffer
+        -- Train generator off yielded high-level transitions + summed low-level discriminator rewards
+
+TESTING
+----------
+1. Save average vehicle velocities for all expert vehicles (loop roundabout + track + vehicle, average over time)
+
+2. Run test suite for: expert, BC, GAIL, RAIL, HGAIL, (and hopefully HRAIL)
+-- For all roundabouts, tracks
+    -- Get expert velocities for track
+    -- Simulate incrementing agent environment (e.g. on reset, agent +=1)
+        -- Store low-level true joint states, actions, and controlled vehicle index
+    -- Per-vehicle statistics (v_all, v_mean, v_shortfall, a_all, jerk_all, n_collisions, T) 
+-- Aggregate statistics + joint 
+
+Problems
+-----------
+Should train without stopping for collisions, however when doing so, end up with policy that always takes decelerate option
+-- It seems safe at the start of each vehicles sim, but actually it isn't since a car will spawn and hit it
+Solutions:
+    -- Hold cars from spawning if their spawn location is full
+    -- Start simulations a few seconds later (after cars clear their spawn places) <- Preferred
+    
+Test could run indefinitely if stop_on_collision is off
+Solution:
+    -- Set maximum episode length in intersimple
+
+
+
+
+
+
+
+
+Save massive set of transition raw states beforehand (1 from training, but with raw states)
+# -- For all roundabouts, tracks
+#     -- For all vehicles, steps
+#         -- Raw vehicle state, action

scratch/arec/intersimple/render_env_from_model.py

@@ -0,0 +1,58 @@
+
+import stable_baselines3 as sb3
+from intersim.envs.intersimple import NRasterized
+
+
+def render_env(model_name='gail_image_multiagent_nocollision', agent=51, environment=NRasterized):
+    """
+    Render a video from an model, agent, and environment
+    Args:
+        model_name (str): name of the model
+        agent (int): agent to start the video from
+        environment (gym.Env): gym environment class to render environment on
+    """
+
+    model = sb3.PPO.load(model_name)
+
+    env = environment(stop_on_collision=False, width=36, height=36, m_per_px=2, agent=agent)
+
+    obs = env.reset()
+    i=0
+    while True and i < 600:
+        i+=1
+        action, _states = model.predict(obs)
+        obs, rewards, done, info = env.step(action)
+        env.render(mode='post')
+        if done:
+            break
+
+    env.close(filestr='render/'+model_name+'_agent%i'%(agent)) 
+
+def render_options_env(model_name='gail_image_multiagent_nocollision', agent=51, environment=NRasterized):
+    """
+    Render a video from an model, agent, and environment
+    Args:
+        model_name (str): name of the model
+        agent (int): agent to start the video from
+        environment (gym.Env): gym environment class to render environment on
+    """
+
+    model = sb3.PPO.load(model_name)
+
+    env = environment(stop_on_collision=False, width=36, height=36, m_per_px=2, agent=agent)
+
+    obs = env.reset()
+    i=0
+    while True and i < 600:
+        i+=1
+        action, _states = model.predict(obs)
+        obs, rewards, done, info = env.step(action)
+        env.render(mode='post')
+        if done:
+            break
+
+    env.close(filestr='render/'+model_name+'_agent%i'%(agent)) 
+
+if __name__ == '__main__':
+    import fire
+    fire.Fire(render_env)

scratch/arec/intersimple/render_options.py

@@ -0,0 +1,11 @@
+import sys
+sys.path.append('../../../')
+from src.util import render_env
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10]]
+
+def render_wrapper(**kwargs):
+    render_env(**kwargs, options_list=ALL_OPTIONS)
+
+if __name__=='__main__':
+    import fire
+    fire.Fire(render_wrapper)

scratch/arec/intersimple/test_model.py

@@ -0,0 +1,148 @@
+from tqdm import tqdm
+from copy import deepcopy
+import stable_baselines3 as sb3
+import intersim
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10]] # option 0 is safe fallback
+
+def load_model(model_path:str, method:str):
+    """
+    Load a model given a path and the method
+    
+    Args:
+        model_path (str): the path to the model
+        method (str): the method for the model
+    Returns:
+        model: the action model
+        is_heir (bool): whether the method is heirarchial
+    """
+    model = None
+    is_heir = False
+    if method == 'expert':
+        raise NotImplementedError
+    elif method == 'bc':
+        raise NotImplementedError
+    elif method == 'gail':
+        raise NotImplementedError
+    elif method == 'rail':
+        raise NotImplementedError
+    elif method == 'hgail':
+        is_heir = True
+        model = sb3.PPO.load(model_path)
+    elif method == 'hrail':
+        is_heir = True
+        raise NotImplementedError
+    else:
+        raise NotImplementedError
+    return model, is_heir
+
+def load_expert_states(roundabout, track):
+    """
+    Load expert states from roundabout/track info
+    Args:
+        roundabout (str): roundabout name
+        track (str): track id
+    Returns:
+        states (torch.tensor): (T+1, nv, 5) expert states for track file
+        actions (torch.tensor): (T, nv, 1) expert actions for track file
+    """
+    state_path = '../../../expert_data/%s/track%04i/joint_expert_states.pt'%(roundabout, track)] #FIXME when moving
+    action_path = '../../../expert_data/%s/track%04i/joint_expert_actions.pt'%(roundabout, track)] #FIXME when moving
+    states = torch.load(path)
+    actions = torch.load(path)
+    # nanify actions where vehicle's don't exist
+    import pdb
+    pdb.set_trace()
+    return states, actions
+
+def test_model(
+    locations=[(0,0)], 
+    model_name='gail_image_multiagent_nocollision', 
+    env='NRasterizedRouteIncrementingAgent', 
+    method='expert',
+    options_list=ALL_OPTIONS, 
+    **env_kwargs):
+    """
+    Test a particular model at different locations/tracks
+
+    Args:
+        locations (list of tuples): list of (roundabout, track) integer pairs
+        model_name (str): name of model to test
+        env (str): environment class
+        method (str): method (expert, bc, gail, rail, hgail, hrail)
+        options_list (list): list of options
+    """
+
+    # load policy
+    policy, is_heir = load_model(model_name, method)
+
+    # iterate through vehicles
+    all_vehicle_infos = []
+    for i, location in tqdm(enumerate(locations)):
+        
+        # add roundabout and track to environent 
+        roundabout, track = location
+        iround = intersim.LOCATIONS.index(roundabout)
+        it_env_kwargs = deepcopy(env_kwargs)
+        loc_kwargs = {
+            'loc':iround,
+            'track':track
+        }
+        it_env_kwargs.update(loc_kwargs)
+        
+        # load expert states and get average velocities
+        expert_states, expert_actions = load_expert_states(roundabout, track)
+        expert_vavg = torch.nanmean(expert_states[:,:,3], dim=-1)
+
+        # initialize environment
+        if not is_heir:
+            Env = src.options.envs.__dict__[env]
+        else: 
+            Env = intersim.envs.intersimple.__dict__[env]
+        env = Env(**env_kwargs)
+        s = env.reset()
+
+        # Iterate through every vehicle and time
+        vehicle_infos, done = [], False
+        for iv in range(env.nv):
+            v_number = env.agent
+            i_vehicle_infos = {'s':[], 'a':[], 'it':[]}
+            while not done:
+                a = policy(s)
+                sp, r, done, info   = env.step(a)
+                i_vehicle_infos['s'].append(env._env.state) # FIX
+                i_vehicle_infos['a'].append(a) 
+                i_vehicle_infos['it'].append(env._env.it) # FIX
+            i_vehicle_info.update({
+                'vehicle_id': env.agent,
+                'n_steps': len(i_vehicle_infos['a']),
+                'T': len(i_vehicle_infos['a'])*env._env.dt, # FIX
+                'n_collisions': collision.check(i_vehicle_infos['s'], env._env.lengths. env._env.widths), # FIX
+                'expert_vavg': expert_vavg[env.agent]
+            })
+            vehicle_infos.append(i_vehicle_info)
+            env.reset()
+
+        all_vehicle_infos.append({
+            'loc': location,
+            'track': track,
+            'stats': vehicle_infos
+            })
+        env.close()
+
+    # print and save model-specific metrics
+    outfolder = 'test_metrics'
+    print_and_save(all_vehicle_infos, method, model, outfolder)
+
+def print_and_save(stats, method, model, outfolder):
+    """
+    Print and save stats
+    """
+    pass
+
+def load_compare():
+    pass
+
+if __name__=='__main__':
+    import fire
+    fire.Fire()

scratch/etienne/intersimple/airl_flat.py

@@ -0,0 +1,64 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import IntersimpleReward
+
+model_name = 'airl_flat'
+
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpert_IntersimpleRewardAgent51.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(IntersimpleReward, n_envs=2, env_kwargs={'agent': 51})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train AIRL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "AIRL/")
+airl_trainer = adversarial.AIRL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=64,
+    gen_algo=sb3.PPO("MlpPolicy", venv, verbose=1, n_steps=1024), # n_steps = 2048 ?
+)
+airl_trainer.train(total_timesteps=100000)
+airl_trainer.gen_algo.save(model_name)
+
+del airl_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = IntersimpleReward(agent=51)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/bc_flat.py

@@ -0,0 +1,59 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import IntersimpleReward
+
+model_name = 'bc_flat'
+
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpert_IntersimpleRewardAgent51.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(IntersimpleReward, n_envs=2, env_kwargs={'agent': 51})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train BC on expert data.
+# BC also accepts as `expert_data` any PyTorch-style DataLoader that iterates over
+# dictionaries containing observations and actions.
+logger.configure(tempdir_path / "BC/")
+bc_trainer = bc.BC(venv.observation_space, venv.action_space, expert_data=transitions)
+bc_trainer.train(n_epochs=1000)
+bc_trainer.save_policy(model_name)
+
+del bc_trainer
+
+# %%
+model = bc.reconstruct_policy(model_name)
+
+env = IntersimpleReward(agent=51)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/data/expert.py

@@ -0,0 +1,138 @@
+from intersim.envs.intersimple import Intersimple, InfoFilter
+from stable_baselines3.common.policies import BasePolicy
+import gym
+from intersim.envs.intersimple import *
+from gail.envs import *
+import imitation.data.rollout as rollout
+from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv
+from imitation.data.wrappers import RolloutInfoWrapper
+
+class IntersimExpert(BasePolicy):
+    
+    def __init__(self, intersim_env, mu=0, *args, **kwargs):
+        super().__init__(
+            observation_space=gym.spaces.Space(),
+            action_space=gym.spaces.Space(),
+            *args, **kwargs
+        )
+        self._intersim = intersim_env
+        self._mu = mu
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _action(self):
+        target_t = min(self._intersim._ind + 1, len(self._intersim._svt.simstate) - 1)
+        target_state = self._intersim._svt.simstate[target_t]
+        return self._intersim.target_state(target_state, mu=self._mu)
+
+    def predict(self, *args, **kwargs):
+        return self._action(), None
+
+class IntersimpleExpert(BasePolicy):
+
+    def __init__(self, intersimple_env, mu=0, *args, **kwargs):
+        super().__init__(
+            observation_space=intersimple_env.observation_space,
+            action_space=intersimple_env.action_space,
+            *args, **kwargs
+        )
+        self._intersimple = intersimple_env
+        self._intersim_expert = IntersimExpert(intersimple_env._env, mu=mu)
+    
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _action(self):
+        # RandomLocation mixin re-initializes the intersim sub-env
+        self._intersim_expert._intersim = self._intersimple._env
+        return self._intersim_expert._action()[self._intersimple._agent]
+
+    def predict(self, *args, **kwargs):
+        return self._action(), None
+
+class NormalizedIntersimpleExpert(IntersimpleExpert):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def predict(self, *args, **kwargs):
+        action, _ = super().predict(*args, **kwargs)
+        return self._intersimple._normalize(action), None
+
+class DummyVecEnvPolicy(BasePolicy):
+
+    def __init__(self, experts):
+        self._experts = [e() for e in experts]
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+    
+    def predict(self, *args, **kwargs):
+        predictions = [e.predict() for e in self._experts]
+        actions = [p[0] for p in predictions]
+        states = [p[1] for p in predictions]
+        return actions, states
+    
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+
+def save_video(env, expert):
+    env.reset()
+    env.render()
+    done = False
+    while not done:
+        actions, _ = expert.predict()
+        _, _, done, _ = env.step(actions)
+        env.render()
+    env.close()
+
+def demonstrations(expert='NormalizedIntersimpleExpert', env='NRasterizedRandomAgent', path=None, min_timesteps=25000, min_episodes=None, video=False, env_args={}, policy_args={}):
+    """Rollout and save expert demos.
+    
+    Usage:
+        python -m intersimple.expert <flags>
+
+    """
+    Env = globals()[env]
+    Expert = globals()[expert]
+
+    env = Env(**env_args)
+    info_env = RolloutInfoWrapper(env)
+    venv = DummyVecEnv([lambda: info_env])
+
+    policy = Expert(env, **policy_args)
+    venv_policy = DummyVecEnvPolicy([lambda: policy])
+
+    if video:
+        save_video(env, policy)
+    
+    path = path or (policy.__class__.__name__ + '_' + env.__class__.__name__ + '.pkl')
+    include_infos = isinstance(env, InfoFilter)
+
+    rollout.rollout_and_save(
+        path=path,
+        policy=venv_policy,
+        venv=venv,
+        sample_until=rollout.make_sample_until(
+            min_timesteps=min_timesteps,
+            min_episodes=min_episodes,
+        ),
+        exclude_infos=not include_infos,
+    )
+
+if __name__ == '__main__':
+    import fire
+    fire.Fire(demonstrations)

scratch/etienne/intersimple/data/generate.sh

@@ -0,0 +1,15 @@
+#python -m expert --env=IntersimpleReward --min_timesteps=200 --env_args='{agent:51}' --path='NormalizedIntersimpleExpert_IntersimpleRewardAgent51.pkl'
+#python -m expert --env=IntersimpleReward --min_timesteps=200 --env_args='{agent:51}' --policy_args='{mu:0.005}' --path='NormalizedIntersimpleExpert_IntersimpleRewardAgent51Mu.005.pkl'
+#python -m expert --env=IntersimpleReward --min_timesteps=200 --env_args='{agent:51}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpert_IntersimpleRewardAgent51Mu.001.pkl'
+#python -m expert --env=NRasterized --min_timesteps=200 --env_args='{agent:51,width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl'
+#python -m expert --env=NRasterized --min_timesteps=200 --env_args='{agent:51,width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl'
+#python -m expert --env=NRasterized --min_timesteps=3000 --video --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedRandomAgentw36h36mppx2.pkl'
+#python -m expert --env=NRasterizedRandomAgent --min_timesteps=200 --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001_NRasterizedRandomAgentw36h36mppx2.pkl'
+#python -m expert --env=NRasterizedRandomAgent --min_timesteps=10000 --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N10000_NRasterizedRandomAgentw36h36mppx2.pkl'
+#python -m expert --env=NRasterizedRouteRandomAgent --min_timesteps=10000 --env_args='{width:70,height:70,m_per_px:1}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N10000_NRasterizedRouteRandomAgentw70h70mppx1.pkl'
+#python -m expert --env=NRasterizedRouteRandomAgentLocation --min_timesteps=100000 --env_args='{width:70,height:70,m_per_px:1}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N100000_NRasterizedRouteRandomAgentLocationw70h70mppx1.pkl'
+#python -m expert --env=NRasterizedRouteRandomAgentLocation --min_timesteps=100000 --env_args='{width:70,height:70,m_per_px:1,map_color:128}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N100000_NRasterizedRouteRandomAgentLocationw70h70mppx1mapc128.pkl'
+#python -m expert --env=NRasterizedRouteSpeedRandomAgentLocation --min_timesteps=10000 --env_args='{width:70,height:70,m_per_px:1,map_color:128,mu:0.001}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N10000_NRasterizedRouteSpeedRandomAgentLocationw70h70mppx1mapc128mu.001.pkl'
+#python -m data.expert --env=NRasterizedRouteSpeedRandomAgentLocation --min_timesteps=10000 --env_args='{width:70,height:70,m_per_px:1,map_color:128,mu:0.001,skip_frames:5}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N10000_NRasterizedRouteSpeedRandomAgentLocationw70h70mppx1mapc128mu.001skip5.pkl'
+#python -m data.expert --env=TLNRasterizedRouteRandomAgentLocation --min_timesteps=100000 --env_args='{width:70,height:70,m_per_px:1,mu:0.001,random_skip:True,max_episode_steps:50}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N100000_TLNRasterizedRouteRandomAgentLocationw70h70mppx1mu.001rskips50.pkl'
+python -m data.expert --env=TLNRasterizedRouteRandomAgentLocation --min_timesteps=50000 --env_args='{width:70,height:70,m_per_px:1,mu:0.001,random_skip:True,max_episode_steps:50}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N50000_TLNRasterizedRouteRandomAgentLocationw70h70mppx1mu.001rskips50.pkl'

scratch/etienne/intersimple/gail/envs.py

@@ -0,0 +1,46 @@
+import gym
+from gym.wrappers.time_limit import TimeLimit
+import numpy as np
+from intersim.envs.intersimple import NRasterizedRouteRandomAgentLocation, RandomLocation, RandomAgent, RewardVisualization, Reward, \
+    ImageObservationAnimation, RasterizedRoute, NObservations, RasterizedObservation, \
+    NormalizedActionSpace, ActionVisualization, InteractionSimulatorMarkerViz, ImitationCompat, Intersimple
+
+class RasterizedSpeed:
+
+    def __init__(self, max_speed=12, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        channels, height, width = self.observation_space.shape
+        self.observation_space = gym.spaces.Box(
+            low=0,
+            high=255,
+            shape=(channels+1, height, width),
+            dtype=np.uint8
+        )
+        self._max_speed = max_speed
+
+    def _simple_obs(self, intersim_obs, intersim_info):
+        img = super()._simple_obs(intersim_obs, intersim_info)
+
+        ego_speed = intersim_obs['state'][self._agent, 2]
+        scaled_speed = (255 * ego_speed) // self._max_speed
+        speed_layer = scaled_speed * np.ones_like(img[:1], dtype=np.uint8)
+        speed_layer = speed_layer.clamp(0, 255)
+        
+        obs = np.concatenate((img, speed_layer), axis=0)
+        return obs
+
+class NRasterizedRouteSpeedRandomAgentLocation(RandomLocation, RandomAgent, RewardVisualization,
+        Reward, ImageObservationAnimation, RasterizedRoute, NObservations, RasterizedSpeed, RasterizedObservation,
+        NormalizedActionSpace, ActionVisualization, InteractionSimulatorMarkerViz, ImitationCompat, Intersimple):
+    pass
+
+class TransparentTimeLimit(TimeLimit):
+
+    def __getattr__(self, name):
+        return getattr(self.env, name)
+    
+    def close(self, *args, **kwargs):
+        return self.env.close(*args, **kwargs)
+
+def TLNRasterizedRouteRandomAgentLocation(max_episode_steps, *args, **kwargs):
+    return TransparentTimeLimit(NRasterizedRouteRandomAgentLocation(*args, **kwargs), max_episode_steps=max_episode_steps)

scratch/etienne/intersimple/gail/options2.py

@@ -0,0 +1,127 @@
+import gym
+import torch
+from src.util.collisions import feasible
+import numpy as np
+from collections import deque
+
+def imitation_discriminator(discriminator):
+    return lambda obs, action, next_obs, done: discriminator.discrim_net.predict_reward_train(
+        state=torch.tensor(obs).unsqueeze(0).to(discriminator.discrim_net.device()),
+        action=torch.tensor([[action]]).to(discriminator.discrim_net.device()),
+        next_state=torch.tensor(next_obs).unsqueeze(0).to(discriminator.discrim_net.device()), # unused
+        done=torch.tensor(done).unsqueeze(0).to(discriminator.discrim_net.device()), # unused
+    ).item()
+
+class OptionsEnv(gym.Wrapper):
+
+    def __init__(self, env, options, discriminator, discount, ll_buffer, *args, **kwargs):
+        super().__init__(env, *args, **kwargs)
+
+        self.options = options
+        num_hl_options = len(self.options)
+        self.action_space = gym.spaces.Discrete(num_hl_options)
+        self.observation_space = gym.spaces.Dict({
+            'obs': env.observation_space,
+            'mask': gym.spaces.Box(low=0, high=1, shape=(num_hl_options,)),
+        })
+
+        self.discriminator = discriminator
+        self.discount = discount
+        self.ll_buffer = ll_buffer
+    
+    @staticmethod
+    def _hl_observation(obs, mask):
+        return {
+            'obs': obs,
+            'mask': mask,
+        }
+    
+    def reset(self):
+        self.done = False
+        self.obs = self.env.reset()
+        self.m = available_actions(self.env, self.options)
+        return self._hl_observation(self.obs, self.m)
+    
+    def _ll_step(self, action):
+        return self.env.step(action)
+    
+    def step(self, action):
+        assert self.m[action]
+        assert not self.done
+
+        plan = list(map(float, generate_plan(self.env, action, self.options)))
+        reward = 0
+        steps = 0
+
+        while not self.done and plan and \
+            (feasible(self.env, safety_plan(self.env, plan)) or self.m.sum() == 1):
+            
+            a, plan = plan[0], plan[1:]
+            a = self.env._normalize(a)
+
+            next_obs, _, self.done, info = self._ll_step(a)
+
+            reward += self.discount**steps * self.discriminator(self.obs, a, next_obs, self.done)
+
+            self.ll_buffer.append({
+                'obs': self.obs,
+                'next_obs': next_obs,
+                'acts': np.array((a,)),
+                'dones': np.array(self.done),
+            })
+
+            steps += 1
+            self.obs = next_obs
+
+        self.m = available_actions(self.env, self.options)
+
+        return self._hl_observation(self.obs, self.m), reward, self.done, info
+
+class RenderOptions(OptionsEnv):
+
+    def __init__(self, env, options, *args, **kwargs):
+        super().__init__(env, options, discriminator=lambda s, a, n, d: 0, discount=1, ll_buffer=deque(maxlen=0), *args, **kwargs)
+
+    def _ll_step(self, action):
+        out = super()._ll_step(action)
+        self.env.render(mode='post')
+        return out
+    
+    def close(self, *args, **kwargs):
+        self.env.close(*args, **kwargs)
+
+def safety_plan(env, plan):
+    return np.concatenate((plan, np.array(5 * [env._env._min_acc])), axis=0)
+
+def available_actions(env, options):
+    """Return mask of available actions given current `env` state.
+    Action 0 is considered safe fallback.
+    """
+    plans = [generate_plan(env, i, options) for i, _ in enumerate(options)]
+    # is emergency braking still possible?
+    plans = list(map(lambda p: safety_plan(env, p), plans))
+
+    T = max(len(p) for p in plans)
+    plans = [np.pad(p, ((0, T-len(p)),), constant_values=np.nan) for p in plans]
+    plans = np.stack(plans, axis=0)
+    
+    valid = feasible(env, plans)
+    if not valid.any():
+        valid[0] = True
+
+    return valid
+
+def target_velocity_plan(current_v: float, target_v: float, t: int, dt: float):
+    """Smoothly target a velocity in a given number of steps"""
+    # for now, constant acceleration
+    a = (target_v - current_v)  / (t * dt)
+    return a*np.ones((t,))
+
+def generate_plan(env, i, options):
+    """Generate input profile for high-level action `i`."""
+    assert i < len(options), "Invalid option index {i}"
+    target_v, t = options[i]
+    current_v = env._env.state[env._agent, 1].item() # extract from env
+    plan = target_velocity_plan(current_v, target_v, t, env._env._dt)
+    assert len(plan) == t, "incorrect plan length"
+    return plan

scratch/etienne/intersimple/gail_flat.py

@@ -0,0 +1,70 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import IntersimpleReward
+
+from gail.discriminator import MlpDiscriminator
+
+model_name = 'gail_flat'
+
+# %%
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpert_IntersimpleRewardAgent51.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(IntersimpleReward, n_envs=2, env_kwargs={'agent': 51})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train GAIL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "GAIL/")
+gail_trainer = adversarial.GAIL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=150,
+    n_disc_updates_per_round=32,
+    discrim_kwargs={'discrim_net': MlpDiscriminator()},
+    gen_algo=sb3.PPO("MlpPolicy", venv, verbose=1, n_steps=4530),
+    allow_variable_horizon=True,
+)
+gail_trainer.train(total_timesteps=400000)
+gail_trainer.gen_algo.save(model_name)
+
+#del gail_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = IntersimpleReward(agent=51)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/gail_flat_ray.py

@@ -0,0 +1,115 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import IntersimpleReward, speed_reward
+
+from gail.discriminator import MlpDiscriminator
+import numpy as np
+import functools
+from stable_baselines3.common.evaluation import evaluate_policy
+from ray import tune
+import os
+import torch
+
+model_name = 'gail_flat'
+
+# %%
+# Load pickled test demonstrations.
+#with open("data/NormalizedIntersimpleExpert_IntersimpleRewardAgent51.pkl", "rb") as f:
+with open("data/NormalizedIntersimpleExpert_IntersimpleRewardAgent51Mu.001.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(IntersimpleReward, n_envs=2, env_kwargs={'agent': 51})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+def training_function(config, checkpoint_dir=None):
+    logger.configure(tempdir_path / "GAIL/")
+
+    discriminator = MlpDiscriminator()
+    if checkpoint_dir:
+        discriminator.load_state_dict(torch.load(os.path.join(checkpoint_dir, 'disc_checkpoint')))
+        generator = sb3.PPO.load(os.path.join(checkpoint_dir, 'gen_checkpoint'))
+    else:
+        generator = sb3.PPO("MlpPolicy", venv, verbose=1, n_steps=config['n_steps'])
+
+    gail_trainer = adversarial.GAIL(
+        venv,
+        expert_data=transitions,
+        expert_batch_size=config['expert_batch_size'],
+        n_disc_updates_per_round=config['n_disc_updates_per_round'],
+        discrim_kwargs={'discrim_net': MlpDiscriminator()},
+        gen_algo=generator,
+        allow_variable_horizon=True,
+    )
+
+    def callback(epoch):
+        print("callback")
+        eval_env = IntersimpleReward(agent=51, reward=functools.partial(speed_reward, collision_penalty=0.))
+        #sync_envs_normalization(self.training_env, self.eval_env)
+        episode_rewards, episode_lengths = evaluate_policy(generator, eval_env, return_episode_rewards=True)
+        tune.report(
+            reward=np.mean(episode_rewards),
+            length=np.mean(episode_lengths),
+            training_iteration=epoch,
+        )
+
+        with tune.checkpoint_dir(step=epoch) as checkpoint_dir:
+            gail_trainer.gen_algo.save(os.path.join(checkpoint_dir, 'gen_checkpoint'))
+            torch.save(discriminator.state_dict(), os.path.join(checkpoint_dir, 'disc_checkpoint'))
+
+    gail_trainer.train(total_timesteps=40000, callback=callback)
+
+analysis = tune.run(
+    training_function,
+    config = {
+        'expert_batch_size': tune.randint(1, 22), #220,
+        'n_disc_updates_per_round': tune.randint(2, 100), #16,
+        'n_steps': tune.randint(1, 10000), #4096,
+    },
+    resources_per_trial={
+        'cpu': 1,
+        # 'gpu': 1,
+    },
+    local_dir='ray',
+    num_samples=10,
+)
+
+print('Best config', analysis.get_best_config(metric='progress', mode='max'))
+
+#del gail_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = IntersimpleReward(agent=51)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/gail_image.py

@@ -0,0 +1,70 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import NRasterized
+
+from gail.discriminator import CnnDiscriminator
+
+model_name = 'gail_image'
+
+# %%
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(NRasterized, n_envs=2, env_kwargs={'agent': 51, 'width': 36, 'height': 36, 'm_per_px': 2})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train GAIL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "GAIL/")
+gail_trainer = adversarial.GAIL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    #n_disc_updates_per_round=2048,
+    discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+    gen_algo=sb3.PPO("CnnPolicy", venv, verbose=1, n_steps=1024),
+    allow_variable_horizon=True,
+)
+gail_trainer.train(total_timesteps=100000)
+gail_trainer.gen_algo.save(model_name)
+
+#del gail_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = NRasterized(agent=51, width=36, height=36, m_per_px=2)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/gail_image_multiagent_nocollision.py

@@ -0,0 +1,70 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import NRasterized
+
+from gail.discriminator import CnnDiscriminatorFlatAction
+
+model_name = 'gail_image_multiagent_nocollision'
+
+# %%
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedRandomAgentw36h36mppx2.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(NRasterized, n_envs=2, env_kwargs={'stop_on_collision':False, 'width': 36, 'height': 36, 'm_per_px': 2})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train GAIL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "GAIL/")
+gail_trainer = adversarial.GAIL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    #n_disc_updates_per_round=2048,
+    discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+    gen_algo=sb3.PPO("CnnPolicy", venv, verbose=1, n_steps=1024),
+    allow_variable_horizon=True,
+)
+gail_trainer.train(total_timesteps=100000)
+gail_trainer.gen_algo.save(model_name)
+
+#del gail_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = NRasterized(stop_on_collision=False, width=36, height=36, m_per_px=2)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/gail_image_random.py

@@ -0,0 +1,79 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import NRasterizedRandomAgent, IntersimpleReward, speed_reward
+import functools
+from stable_baselines3.common.evaluation import evaluate_policy
+
+from gail.discriminator import CnnDiscriminator
+
+model_name = 'gail_image_random'
+
+# %%
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedRandomAgentw36h36mppx2.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+env_kwargs = {'width': 36, 'height': 36, 'm_per_px': 2}
+venv = make_vec_env(NRasterizedRandomAgent, n_envs=2, env_kwargs=env_kwargs)
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train GAIL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "GAIL/")
+generator = sb3.PPO("CnnPolicy", venv, verbose=1, n_steps=1024)
+gail_trainer = adversarial.GAIL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    #n_disc_updates_per_round=2048,
+    discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+    gen_algo=generator,
+    allow_variable_horizon=True,
+)
+def callback(round):
+    eval_env = NRasterizedRandomAgent(reward=functools.partial(speed_reward, collision_penalty=0.), **env_kwargs)
+    #sync_envs_normalization(self.training_env, self.eval_env)
+    episode_rewards, episode_lengths = evaluate_policy(generator, eval_env, return_episode_rewards=True)
+
+gail_trainer.train(total_timesteps=100000, callback=callback)
+gail_trainer.gen_algo.save(model_name)
+
+#del gail_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = NRasterizedRandomAgent(width=36, height=36, m_per_px=2)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/gail_image_random_ray.py

@@ -0,0 +1,171 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+import os
+import random
+import numpy as np
+import torch
+
+# set up ray tune
+import ray
+from ray import tune
+from ray.tune import Analysis, ExperimentAnalysis
+from ray.tune.schedulers import ASHAScheduler
+from ray.tune.suggest.hyperopt import HyperOptSearch
+from ray.tune.suggest import ConcurrencyLimiter
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import NRasterizedRandomAgent, IntersimpleReward, speed_reward, NRasterized, NRasterizedRandomAgentVerbose
+import functools
+from stable_baselines3.common.evaluation import evaluate_policy
+from gym.wrappers import TimeLimit
+
+from gail.discriminator import CnnDiscriminator
+
+model_name = 'gail_image_random_ray'
+env_kwargs={'width': 36, 'height': 36, 'm_per_px': 2}
+
+# %%
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("--outdir", help="result directory", default='ray')
+parser.add_argument("--test", help="test run", default=False, action="store_true")
+args = parser.parse_args()
+outdir = args.outdir
+
+# %%
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpertMu.001N10000_NRasterizedRandomAgentw36h36mppx2.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+# Store transitions in shared ray memory
+ray_transitions = ray.put(transitions)
+
+# %%
+venv = make_vec_env(NRasterizedRandomAgent, n_envs=2, env_kwargs=env_kwargs)
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+logger.configure(tempdir_path / "GAIL/")
+
+def get_ray_config(test=False):
+    if test:
+        return {
+            'expert_batch_size': 2,
+            'ppo_n_steps': 2,
+            'ppo_batch_size': 2,
+            'ppo_n_epochs': 1,
+            'total_timesteps': 10,
+        }
+    else:
+        return {
+            'expert_batch_size': tune.choice([2**x for x in range(6,10)]),
+            'ppo_n_steps': tune.choice([2048, 3072, 4096]),
+            'ppo_batch_size': tune.choice([2**x for x in range(9,13)]),
+            'ppo_n_epochs': tune.choice([6,10]),
+            'total_timesteps': 400_000,
+        }
+
+
+def ray_train(config, checkpoint_dir=None):
+    # Train GAIL on expert data.
+    # GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+    # iterates over dictionaries containing observations, actions, and next_observations.
+
+    discriminator = CnnDiscriminator(venv)
+    if checkpoint_dir:
+        discriminator.load_state_dict(torch.load(os.path.join(checkpoint_dir, 'disc_checkpoint')))
+        generator = sb3.PPO.load(os.path.join(checkpoint_dir, 'gen_checkpoint'))
+    else:
+        generator = sb3.PPO(
+            "CnnPolicy", venv, verbose=0, 
+            n_steps=config["ppo_n_steps"], 
+            batch_size=config["ppo_batch_size"], 
+            n_epochs=config["ppo_n_epochs"]
+        )
+    gail_trainer = adversarial.GAIL(
+        venv,
+        expert_data=ray.get(ray_transitions),
+        expert_batch_size=config["expert_batch_size"],
+        #n_disc_updates_per_round=2048,
+        discrim_kwargs={'discrim_net': discriminator},
+        gen_algo=generator,
+        allow_variable_horizon=True,
+    )
+    def callback(round):
+        # eval_env = NRasterized(agent=51, reward=functools.partial(speed_reward, collision_penalty=0.), **env_kwargs)
+        eval_env = TimeLimit(NRasterizedRandomAgent(reward=functools.partial(speed_reward, collision_penalty=0.), **env_kwargs), max_episode_steps=1000)
+        episode_rewards, episode_lengths = evaluate_policy(generator, eval_env, return_episode_rewards=True)
+        tune.report(
+            reward=np.mean(episode_rewards),
+            length=np.mean(episode_lengths),
+            training_iteration=round,
+        )
+        with tune.checkpoint_dir(step=round) as checkpoint_dir:
+            gail_trainer.gen_algo.save(os.path.join(checkpoint_dir, 'gen_checkpoint'))
+            torch.save(discriminator.state_dict(), os.path.join(checkpoint_dir, 'disc_checkpoint'))
+
+    gail_trainer.train(total_timesteps=config['total_timesteps'], callback=callback)
+
+
+ray_config = get_ray_config(args.test)
+search = HyperOptSearch(ray_config, metric='length', mode="max",)
+search = ConcurrencyLimiter(search, max_concurrent=10)
+custom_scheduler = ASHAScheduler(time_attr='training_iteration', metric='length', mode="max", grace_period=15)
+
+analysis = tune.run(
+    ray_train, 
+    # config=ray_config,
+    search_alg=search,
+    scheduler=custom_scheduler,
+    local_dir=outdir,
+    resources_per_trial={"cpu":10, "gpu": 0.2},
+    num_samples=1 if args.test else 100,
+)
+
+del analysis
+
+# %%
+# outdir = "ray/ray_train_2021-09-20_13-33-50/ray_train_f06785b0_33_expert_batch_size=128,ppo_batch_size=1024,ppo_n_epochs=6,ppo_n_steps=2048,total_timesteps=400000_2021-09-20_15-52-05"
+
+# %%
+analysis = Analysis(outdir, default_metric="length", default_mode="max")
+filepath = analysis.get_best_logdir()
+print("Best ray experiment:", filepath)
+config = analysis.get_best_config()
+print("Best config:", config)
+
+# %%
+
+model = sb3.PPO.load(os.path.join(analysis.get_last_checkpoint(), 'gen_checkpoint'))
+
+# env = NRasterized(agent=51, **env_kwargs)
+env = TimeLimit(NRasterizedRandomAgent(reward=functools.partial(speed_reward, collision_penalty=0.), **env_kwargs), max_episode_steps=1000)
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.env.close(filestr='render/'+model_name)
+# %%
+

scratch/etienne/intersimple/gail_image_singleagent_nocollision.py

@@ -0,0 +1,70 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import NRasterized
+
+from gail.discriminator import CnnDiscriminator
+
+model_name = 'gail_image_singleagent_nocollision'
+
+# %%
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(NRasterized, n_envs=2, env_kwargs={'agent':51, 'stop_on_collision':False, 'width': 36, 'height': 36, 'm_per_px': 2})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train GAIL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "GAIL/")
+gail_trainer = adversarial.GAIL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    #n_disc_updates_per_round=2048,
+    discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+    gen_algo=sb3.PPO("CnnPolicy", venv, verbose=1, n_steps=1024),
+    allow_variable_horizon=True,
+)
+gail_trainer.train(total_timesteps=100000)
+gail_trainer.gen_algo.save(model_name)
+
+#del gail_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = NRasterized(agent=51, width=36, height=36, m_per_px=2, stop_on_collision=False)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/gail_options_image.py

@@ -0,0 +1,90 @@
+# %%
+import sys
+sys.path.append('../../../')
+
+from src.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
+from imitation.algorithms import adversarial
+import stable_baselines3
+import torch.utils.data
+import numpy as np
+from intersim.envs.intersimple import NRasterized
+import itertools
+from torch.distributions import Categorical
+import gym
+import torch
+import pickle
+import imitation.data.rollout as rollout
+import tempfile
+import pathlib
+from imitation.util import logger
+from stable_baselines3.common.env_util import make_vec_env
+from tqdm import tqdm
+from src.policies.options import OptionsCnnPolicy
+from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
+from src.gail.train import train_discriminator, train_generator
+
+model_name = 'gail_options_image'
+env_settings = {'agent': 51, 'width': 36, 'height': 36, 'm_per_px': 2}
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10]] # option 0 is safe fallback
+
+def train(expert_data, epochs=20, expert_batch_size=32, generator_steps=1024, discount=0.99):
+    env = NRasterized(**env_settings)
+    env.discount = discount
+
+    tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+    tempdir_path = pathlib.Path(tempdir.name)
+    logger.configure(tempdir_path / "GAIL/")
+    print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+    venv = make_vec_env(NRasterized, n_envs=1, env_kwargs=env_settings)
+    discriminator = adversarial.GAIL(
+        expert_data=expert_data,
+        expert_batch_size=expert_batch_size,
+        discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+        #discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        venv=venv, # unused
+        gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+    )
+
+    generator = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env, options=ALL_OPTIONS),
+        verbose=1,
+        n_steps=generator_steps,
+    )
+
+    # PPO.train requires logger as set up in
+    # PPO._setup_learn (called by PPO.learn)
+    generator._logger = stable_baselines3.common.utils.configure_logger(
+        generator.verbose,
+        generator.tensorboard_log,
+    )
+
+    for _ in tqdm(range(epochs)):
+        train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size)
+        train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
+    
+    return generator
+
+# %%
+if __name__ == '__main__':
+    # %%
+ 
+    with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
+        trajectories = pickle.load(f)
+    transitions = rollout.flatten_trajectories(trajectories)
+    generator = train(transitions)
+
+    generator.save(model_name)
+
+    # %%
+    model = stable_baselines3.PPO.load(model_name)
+
+    env = RenderOptions(NRasterized(**env_settings), options=ALL_OPTIONS)
+
+    for s in env.sample_ll(model):
+        if s['dones']:
+            break
+
+    env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/gail_options_image_alltracks.py

@@ -0,0 +1,410 @@
+# %%
+import sys
+sys.path.append('../../../')
+from src.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
+from src.policies import OptionsCnnPolicy
+from src.util import feasible
+from src.data import load_experts
+
+from imitation.algorithms import adversarial
+from imitation.util import logger
+import imitation.data.rollout as rollout
+
+import stable_baselines3
+from stable_baselines3.common.env_util import make_vec_env
+
+import torch
+import torch.utils.data
+import numpy as np
+import itertools
+import gym
+import pickle
+import tempfile
+import pathlib
+from tqdm import tqdm
+
+from intersim.envs.intersimple import NRasterized, NRasterizedRandomAgent, NRasterizedIncrementingAgent
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is safe fallback
+
+class OptionsEnv(gym.Wrapper):
+    """
+    Wrap an intersimple environment with an options generator
+    """
+    def __init__(self, env, *args, **kwargs):
+        """
+        Initialize wrapped environment and set high-level action and observation spaces
+        """
+        super().__init__(env, *args, **kwargs)
+        num_hl_options = len(ALL_OPTIONS)
+        self.action_space = gym.spaces.Discrete(num_hl_options)
+        self.observation_space = gym.spaces.Dict({
+            'obs': env.observation_space,
+            'mask': gym.spaces.Box(low=0, high=1, shape=(num_hl_options,)),
+        })
+
+    def _after_choice(self):
+        pass
+    
+    def _after_step(self):
+        pass
+
+    def _transitions(self):
+        raise NotImplementedError('Use `LLOptions` or `HLOptions` for sampling.')
+    
+    def sample(self, generator):
+        """
+        yield transitions using a generator
+        Args:
+            generator (sb3.PPO)
+        Yields:
+
+        """
+        self.done = True
+        while True:
+            self.episode_start = False
+            if self.done:
+                self.s = self.env.reset()
+                self.done = False
+                self.episode_start = True
+
+            self.m = available_actions(self.env)
+            self.ch, self.value, self.log_prob = generator.policy.predict({
+                'obs': torch.tensor(self.s).unsqueeze(0).to(generator.policy.device),
+                'mask': torch.tensor(self.m).unsqueeze(0).to(generator.policy.device),
+            })
+            self.plan = list(map(float, generate_plan(self.env, self.ch)))
+
+            self._after_choice()
+
+            assert not self.done
+            assert self.plan
+            #assert feasible(self.env, self.plan, self.ch)
+
+            while not self.done and self.plan and feasible(self.env, self.plan, self.ch):
+                self.a, self.plan = self.plan[0], self.plan[1:]
+                self.a = self.env._normalize(self.a)
+                self.nexts, _, self.done, _ = self.env.step(self.a)
+
+                self._after_step()
+
+                self.s = self.nexts
+            
+            yield from self._transitions()
+
+class LLOptions(OptionsEnv):
+    """Sample low-level (state, action) tuples for discriminator training."""
+    
+    def __init__(self, *args, **kwargs):
+        """
+        LLOption uses the true LL observations
+        """
+        super().__init__(*args, **kwargs)
+        # overwrite observation space to just output obs directly
+        self.observation_space = self.observation_space['obs']
+
+    def _after_choice(self):
+        """
+        After each option choice, initialize/reset the transition buffer
+        """
+        self._transition_buffer = []
+
+    def _after_step(self):
+        """
+        After each ll action, append s, s', a, done to transition buffer
+        """
+        self._transition_buffer.append({
+            'obs': self.s,
+            'next_obs': self.nexts,
+            'acts': np.array((self.a,)),
+            'dones': np.array(self.done),
+        })
+
+    def _transitions(self):
+        """
+        Yield from the transition buffer
+        """
+        yield from self._transition_buffer
+    
+    def sample_ll(self, policy):
+        """
+        Args:
+            policy
+        Returns:
+            gen: iterable which samples low-level transitions from the environment
+        """
+        return self.sample(policy)
+
+class HLOptions(OptionsEnv):
+    """Sample high-level (state, action, reward) tuples for generator training."""
+    
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def _after_choice(self):
+        """
+        After an option selection, initialize total reward and number of steps
+        """
+        self.obs = {'obs': np.copy(self.s), 'mask': np.copy(self.m)}
+        self.r = 0
+        self.steps = 0
+
+    def _after_step(self):
+        """
+        After each low-level action, add the discounted discriminated reward score (given a discriminator)
+        """
+        self.r += self.discount**self.steps * self.discriminator.discrim_net.reward_train(
+            state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()),
+            action=torch.tensor([[self.a]]).to(self.discriminator.discrim_net.device()),
+            next_state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
+            done=torch.tensor(self.done).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
+        )
+        self.steps += 1
+    
+    def _transitions(self):
+        """
+        Yield a single dictionary per high-level selected action
+        Fields:
+            obs: high-level state and mask at selection
+            action: chosen high-level action
+            reward: accumulated option reward
+            episode_start: whether the action was chosen at the episode start
+            value: the value estimate from the starting state
+            log_prob: the log_prob of the selected action from the starting state
+            done: whether the episode has ended
+
+        """
+        yield {
+            'obs': self.obs,
+            'action': self.ch,
+            'reward': self.r.detach(),
+            'episode_start': self.episode_start,
+            'value': self.value.detach(),
+            'log_prob': self.log_prob.detach(),
+            'done': self.done,
+        }
+    
+    def sample_hl(self, policy, discriminator):
+        """
+        Args:
+            policy
+            discriminator: function with which to score rewards
+        Returns:
+            gen: iterable which samples high-level transitions from the environment
+        """
+        self.discriminator = discriminator
+        return self.sample(policy)
+
+class RenderOptions(LLOptions):
+
+    def _after_step(self):
+        """
+        Render the environment after each low-level step
+        """
+        super()._after_step()
+        self.env.render()
+    
+    def close(self, *args, **kwargs):
+        """
+        On 'close', close the environment
+        """
+        self.env.close(*args, **kwargs)
+
+def available_actions(env):
+    """Return mask of available actions given current `env` state."""
+    valid = np.array([feasible(env, generate_plan(env, i), i) for i in range(len(ALL_OPTIONS))])
+    return valid
+
+def target_velocity_plan(current_v: float, target_v: float, t: int, dt: float):
+    """Smoothly target a velocity in a given number of steps"""
+    # for now, constant acceleration
+    a = (target_v - current_v)  / (t * dt)
+    return a*np.ones((t,))
+
+def generate_plan(env, i):
+    """Generate input profile for high-level action `i`.
+
+    Args:
+        env (gym.Env): current environment state
+        i (int): high-level action `i`
+    Returns:
+        plan (np.array): length T array of acceleration values
+    """
+    assert i < len(ALL_OPTIONS), "Invalid option index {i}"
+    target_v, t = ALL_OPTIONS[i]
+    current_v = env._env.state[env._agent, 1].item() # extract from env
+    plan = target_velocity_plan(current_v, target_v, t, env._env._dt)
+    assert len(plan) == t, "incorrect plan length"
+    return plan
+
+def flatten_transitions(transitions):
+    return {
+        'obs': np.stack(list(t['obs'] for t in transitions), axis=0),
+        'next_obs': np.stack(list(t['next_obs'] for t in transitions), axis=0),
+        'acts': np.stack(list(t['acts'] for t in transitions), axis=0),
+        'dones': np.stack(list(t['dones'] for t in transitions), axis=0),
+    }
+
+def train_discriminator(env, generator, discriminator, num_samples):
+    transitions = list(itertools.islice(env.sample_ll(generator), num_samples))
+    generator_samples = flatten_transitions(transitions)
+    discriminator.train_disc(gen_samples=generator_samples)
+
+def train_generator(env, generator, discriminator, num_samples):
+    generator_samples = list(itertools.islice(env.sample_hl(generator, discriminator), num_samples+1))
+    
+    generator.rollout_buffer.reset()
+    for s in generator_samples[:-1]:
+        generator.rollout_buffer.add(
+            obs=s['obs'],
+            action=s['action'].cpu(),
+            reward=s['reward'].cpu(),
+            episode_start=s['episode_start'],
+            value=s['value'],
+            log_prob=s['log_prob'],
+        )
+    
+    generator.rollout_buffer.compute_returns_and_advantage(
+        last_values=generator_samples[-1]['value'],
+        dones=generator_samples[-1]['done'],
+    )
+    
+    generator.train()
+
+def train(expert_data, env_class=NRasterizedRandomAgent, env_settings={}, epochs=10, discrim_batch_size=32, generator_steps=2048, discount=0.99):
+    """
+    Args: 
+        expert_data: list of transitions
+        env_class: environment class
+        env_settings: environment settings
+        epochs: number of epochs to train for
+        discrim_batch_size: discriminator batch size
+        generator_steps: number of steps taken in generator
+        discount: discount factor
+    Returns:
+        generator (stable_baselines3.PPO): options policy
+    """
+    env = env_class(**env_settings)
+    env.discount = discount
+
+    tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+    tempdir_path = pathlib.Path(tempdir.name)
+    logger.configure(tempdir_path / "GAIL/")
+    print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+    venv = make_vec_env(env_class, n_envs=1, env_kwargs=env_settings)
+    discriminator = adversarial.GAIL(
+        expert_data=expert_data,
+        expert_batch_size=discrim_batch_size,
+        discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+        #discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        venv=venv, # unused
+        gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+    )
+
+    generator = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env),
+        verbose=1,
+        n_steps=generator_steps,
+    )
+
+    # PPO.train requires logger as set up in
+    # PPO._setup_learn (called by PPO.learn)
+    generator._logger = stable_baselines3.common.utils.configure_logger(
+        generator.verbose,
+        generator.tensorboard_log,
+    )
+
+    for _ in tqdm(range(epochs)):
+        train_discriminator(LLOptions(env), generator, discriminator, num_samples=discrim_batch_size)
+        train_generator(HLOptions(env), generator, discriminator, num_samples=generator_steps)
+    
+    return generator
+
+# %%
+if __name__ == '__main__':
+    # %%
+    model_name = 'gail_options_image'
+    env_class = NRasterizedRandomAgent
+    env_settings = {'width': 36, 'height': 36, 'm_per_px': 2}
+    
+    #env_class = NRasterized
+    #env_settings = {'agent': 51, 'width': 36, 'height': 36, 'm_per_px': 2}
+    files = ['../../../expert_data/DR_USA_Roundabout_FT/track%04i/expert.pkl'%(i) for i in range(5)]
+    transitions=load_experts(files)
+
+    generator = train(
+        transitions,
+        env_class=env_class,
+        env_settings=env_settings,
+        epochs=10, 
+        discrim_batch_size=32, 
+        generator_steps=2048, 
+        discount=0.99
+        )
+
+    generator.save(model_name)
+
+    # %%
+    model = stable_baselines3.PPO.load(model_name)
+
+    env = RenderOptions(NRasterizedRandomAgent(**env_args))
+
+    for s in env.sample_ll(model):
+        if s['dones']:
+            break
+
+    env.close(filestr='render/'+model_name)
+
+# %% Tests
+
+def test_ll_expert_data():
+    with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
+        expert_trajectories = pickle.load(f)
+    expert_transitions = rollout.flatten_trajectories(expert_trajectories)
+
+    env = LLOptions(NRasterized(agent=51, width=36, height=36, m_per_px=2))
+
+    gen_transitions = list(itertools.islice(env.sample_ll(
+        policy=stable_baselines3.PPO(
+            OptionsCnnPolicy,
+            OptionsEnv(env),
+            verbose=1,
+        )
+    ), 10))
+    gen_transitions = flatten_transitions(gen_transitions)
+
+    assert expert_transitions[:10].obs.shape == gen_transitions['obs'].shape
+    assert expert_transitions[:10].next_obs.shape == gen_transitions['next_obs'].shape
+    assert expert_transitions[:10].acts.shape == gen_transitions['acts'].shape
+    assert expert_transitions[:10].dones.shape == gen_transitions['dones'].shape
+
+def test_ll_states():
+    env = NRasterized()
+    policy = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env),
+        verbose=1,
+    )
+    llenv = LLOptions(env)
+    transitions = list(itertools.islice(llenv.sample_ll(policy=policy), 100))
+
+    env2 = NRasterized()
+    s2 = env2.reset()
+    for i, t in enumerate(transitions):
+        assert i == 0 or np.array_equal(t['obs'], transitions[i-1]['next_obs'])
+        assert np.array_equal(t['obs'], s2)
+        assert t['acts'].shape == (1,)
+
+        nexts2, _, done2, _ = env2.step(t['acts'])
+        assert np.array_equal(t['next_obs'], nexts2)
+        assert np.array_equal(t['dones'], done2)
+
+        if done2:
+            break
+
+        s2 = nexts2
+
+def test_hl_transitions():
+    pass

scratch/etienne/intersimple/gail_options_image_random.py

@@ -0,0 +1,91 @@
+# %%
+import sys
+sys.path.append('../../../')
+
+from src.discriminator import CnnDiscriminatorFlatAction
+from imitation.algorithms import adversarial
+import stable_baselines3
+import torch.utils.data
+import numpy as np
+from intersim.envs.intersimple import NRasterizedRouteRandomAgent
+import itertools
+from torch.distributions import Categorical
+import gym
+import torch
+import pickle
+import imitation.data.rollout as rollout
+import tempfile
+import pathlib
+from imitation.util import logger
+from stable_baselines3.common.env_util import make_vec_env
+from tqdm import tqdm
+from src.policies.options import OptionsCnnPolicy
+from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
+from src.gail.train import train_discriminator, train_generator
+
+model_name = 'gail_options_image_random'
+env_settings = {'width': 70, 'height': 70, 'm_per_px': 1}
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is safe fallback
+
+def train(expert_data, epochs=100, expert_batch_size=64, generator_steps=1024, discount=0.99):
+    env = NRasterizedRouteRandomAgent(**env_settings)
+    env.discount = discount
+
+    tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+    tempdir_path = pathlib.Path(tempdir.name)
+    logger.configure(tempdir_path / "GAIL/")
+    print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+    venv = make_vec_env(NRasterizedRouteRandomAgent, n_envs=1, env_kwargs=env_settings)
+    discriminator = adversarial.GAIL(
+        expert_data=expert_data,
+        expert_batch_size=expert_batch_size,
+        discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+        #discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        venv=venv, # unused
+        gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+    )
+
+    generator = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env, options=ALL_OPTIONS),
+        verbose=1,
+        n_steps=generator_steps,
+    )
+
+    # PPO.train requires logger as set up in
+    # PPO._setup_learn (called by PPO.learn)
+    generator._logger = stable_baselines3.common.utils.configure_logger(
+        generator.verbose,
+        generator.tensorboard_log,
+    )
+
+    for _ in tqdm(range(epochs)):
+        train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size)
+        train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
+        generator.save(model_name)
+    
+    return generator
+
+def video(model_name, env):
+    model = stable_baselines3.PPO.load(model_name)
+    env = RenderOptions(env, options=ALL_OPTIONS)
+    for s in env.sample_ll(model):
+        if s['dones']:
+            break
+    env.close(filestr='render/'+model_name)
+
+def evaluate():
+    video(
+        model_name=model_name,
+        env=NRasterizedRouteRandomAgent(**env_settings)
+    )
+
+# %%
+if __name__ == '__main__':
+
+    with open("data/NormalizedIntersimpleExpertMu.001N10000_NRasterizedRouteRandomAgentw70h70mppx1.pkl", "rb") as f:
+        trajectories = pickle.load(f)
+    transitions = rollout.flatten_trajectories(trajectories)
+    train(transitions)

scratch/etienne/intersimple/gail_options_image_random_location.py

@@ -0,0 +1,144 @@
+# %%
+from collections import deque
+import sys
+sys.path.append('../../../')
+
+from src.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
+from imitation.algorithms import adversarial
+import stable_baselines3
+import pickle
+import imitation.data.rollout as rollout
+import tempfile
+import pathlib
+from imitation.util import logger
+from tqdm import tqdm
+from src.policies.options import OptionsCnnPolicy
+from src.gail.train import flatten_transitions
+from gail.options2 import OptionsEnv, RenderOptions, imitation_discriminator
+from gail.envs import TLNRasterizedRouteRandomAgentLocation
+from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv
+from stable_baselines3.common.env_util import make_vec_env
+import torch
+import numpy as np
+
+model_name = 'gail_options_image_random_location'
+env_settings = {'width': 70, 'height': 70, 'm_per_px': 1, 'mu': 0.001, 'random_skip': True, 'max_episode_steps': 200}
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,8,10] for t in [5, 10, 20]] # option 0 is safe fallback
+
+class NoisyDiscriminator(CnnDiscriminatorFlatAction):
+
+    def __init__(self, *args, std=0.0, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.std = std
+
+    def forward(self, state, action):
+        noise = self.std * torch.randn(*action.shape, device=action.device)
+        return super().forward(state, action + noise)
+
+class LLBuffer(deque):
+
+    def sample(self, n):
+        assert n <= self.maxlen, f'Sample size of {n} exceeds buffer capacity of {self.maxlen}'
+        assert n <= len(self), f'Sample size of {n} exceeds buffer size of {len(self)}'
+        ind = np.random.randint(len(self), size=n)
+        return list(self[i] for i in ind)
+
+def train(
+        expert_data,
+        expert_batch_size=4096,
+        discriminator_updates_per_round=20,
+        generator_steps=1024,
+        generator_batch_size=1024,
+        generator_total_steps=8192,
+        generator_updates_per_round=10,
+        discount=1.0,
+        epochs=200,
+    ):
+    env = TLNRasterizedRouteRandomAgentLocation(**env_settings)
+
+    tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+    tempdir_path = pathlib.Path(tempdir.name)
+    logger.configure(tempdir_path / "GAIL/")
+    print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+    venv = DummyVecEnv([lambda: env])
+    discriminator = adversarial.GAIL(
+        expert_data=expert_data,
+        expert_batch_size=expert_batch_size,
+        #discrim_kwargs={'discrim_net': NoisyDiscriminator(venv, std=0.25)},
+        disc_opt_cls=torch.optim.RMSprop,
+        disc_opt_kwargs={'lr': 0.0001, 'weight_decay': 0.003},
+        discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        venv=venv, # unused
+        gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+    )
+
+    ll_buffer = LLBuffer(maxlen=expert_batch_size*10)
+
+    options_env = make_vec_env(
+        OptionsEnv,
+        n_envs=1,
+        #vec_env_cls=SubprocVecEnv,
+        env_kwargs={
+            'env': env,
+            'options': ALL_OPTIONS,
+            'discriminator': imitation_discriminator(discriminator),
+            'discount': discount,
+            'll_buffer': ll_buffer,
+        }
+    )
+
+    generator = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        options_env,
+        verbose=1,
+        batch_size=generator_batch_size,
+        n_steps=generator_steps,
+        n_epochs=generator_updates_per_round,
+        gamma=1.0,
+        learning_rate=1e-4,
+    )
+
+    for _ in tqdm(range(epochs)):
+        ll_buffer.clear()
+
+        # train generator
+        generator.learn(total_timesteps=generator_total_steps)
+
+        # train discriminator
+        for _ in range(discriminator_updates_per_round):
+            generator_samples = ll_buffer.sample(expert_batch_size)
+            generator_samples = flatten_transitions(generator_samples)
+            discriminator.train_disc(gen_samples=generator_samples)
+
+        generator.save(model_name)
+    
+    return generator
+
+def video(model_name, env):
+    model = stable_baselines3.PPO.load(model_name)
+
+    done = False
+    obs = env.reset()
+    while not done:
+        action, _ = model.predict(obs)
+        obs, _, done, _ = env.step(action)
+
+    env.close(filestr='render/'+model_name)
+
+def evaluate():
+    video_settings = { **env_settings, 'random_skip': False, 'max_episode_steps': 200 }
+    env = TLNRasterizedRouteRandomAgentLocation(**video_settings)
+    env = RenderOptions(env, options=ALL_OPTIONS)
+    video(
+        model_name=model_name,
+        env=env
+    )
+
+# %%
+if __name__ == '__main__':
+    with open("data/NormalizedIntersimpleExpertMu.001N50000_TLNRasterizedRouteRandomAgentLocationw70h70mppx1mu.001rskips50.pkl", "rb") as f:
+        trajectories = pickle.load(f)
+    transitions = rollout.flatten_trajectories(trajectories)
+    train(transitions)

scratch/etienne/intersimple/imitation_quickstart.py

@@ -0,0 +1,63 @@
+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import IntersimpleReward
+
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpert_IntersimpleRewardAgent51.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(IntersimpleReward, n_envs=2, env_kwargs={'agent': 51})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train BC on expert data.
+# BC also accepts as `expert_data` any PyTorch-style DataLoader that iterates over
+# dictionaries containing observations and actions.
+logger.configure(tempdir_path / "BC/")
+bc_trainer = bc.BC(venv.observation_space, venv.action_space, expert_data=transitions)
+bc_trainer.train(n_epochs=1)
+
+# Train GAIL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "GAIL/")
+gail_trainer = adversarial.GAIL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    gen_algo=sb3.PPO("MlpPolicy", venv, verbose=1, n_steps=1024),
+)
+gail_trainer.train(total_timesteps=2048)
+
+# Train AIRL on expert data.
+logger.configure(tempdir_path / "AIRL/")
+airl_trainer = adversarial.AIRL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    gen_algo=sb3.PPO("MlpPolicy", venv, verbose=1, n_steps=1024),
+)
+airl_trainer.train(total_timesteps=2048)
+
+# %%

scratch/etienne/intersimple/ppo_const.py

@@ -0,0 +1,36 @@
+# %%
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from intersim.envs.intersimple import IntersimpleReward, speed_reward
+
+model_name = "ppo_const"
+
+env = IntersimpleReward(
+    agent=51,
+    #reward=speed_reward,
+)
+
+# %%
+model = PPO(
+    "MlpPolicy", env,
+    verbose=1,
+)
+model.learn(total_timesteps=100000)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/ppo_const_collision.py

@@ -0,0 +1,41 @@
+# %%
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from intersim.envs.intersimple import ConstCollisionReward, IntersimpleFlatAgent
+
+model_name = "ppo_const_collision"
+
+class IntersimpleConstCollisionAgent(ConstCollisionReward, IntersimpleFlatAgent):
+    pass
+
+env = IntersimpleConstCollisionAgent(
+    agent=51,
+    speed_reward_weight=0.001,
+    collision_penalty=1000
+)
+
+# %%
+model = PPO(
+    "MlpPolicy", env,
+    learning_rate=3e-6,
+    verbose=1,
+)
+model.learn(total_timesteps=2e5)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/ppo_const_image.py

@@ -0,0 +1,35 @@
+# %%
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from intersim.envs.intersimple import NRasterized
+
+model_name = "ppo_const_image"
+
+env = NRasterized(
+    agent=51,
+)
+
+# %%
+model = PPO(
+    "CnnPolicy", env,
+    verbose=1,
+)
+model.learn(total_timesteps=100000)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/ppo_const_image_random.py

@@ -0,0 +1,33 @@
+# %%
+from stable_baselines3 import PPO
+from intersim.envs.intersimple import NRasterizedRandomAgent
+import functools
+
+model_name = "ppo_const_image_random"
+
+env = NRasterizedRandomAgent()
+
+# %%
+model = PPO(
+    "CnnPolicy", env,
+    verbose=1,
+)
+model.learn(total_timesteps=2e5)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/ppo_intersimple_tspeed.py

@@ -0,0 +1,25 @@
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from intersim.envs.intersimple import IntersimpleTargetSpeed
+
+env = IntersimpleTargetSpeed()
+
+model = PPO("MlpPolicy", env, verbose=1)
+model.learn(total_timesteps=25000)
+model.save("ppo_intersimple")
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+model = PPO.load("ppo_intersimple")
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close()

scratch/etienne/intersimple/ppo_speed.py

@@ -0,0 +1,46 @@
+# %%
+from stable_baselines3 import PPO
+from intersim.envs.intersimple import IntersimpleReward, speed_reward
+import functools
+
+model_name = "ppo_speed"
+
+#def reward(state, action, info):
+#    speed = state[2].item()
+#    r = speed if speed < 10 else (10 - 5 * (speed - 10))
+#    return 0.1 * r
+
+env = IntersimpleReward(
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+# %%
+model = PPO(
+    "MlpPolicy", env,
+    verbose=1,
+)
+model.learn(total_timesteps=100000)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)
+
+# %%

scratch/etienne/intersimple/ppo_speed_image.py

@@ -0,0 +1,46 @@
+# %%
+from stable_baselines3 import PPO
+from intersim.envs.intersimple import NRasterized, speed_reward
+import functools
+
+model_name = "ppo_speed_image"
+
+#def reward(state, action, info):
+#    speed = state[2].item()
+#    r = speed if speed < 10 else (10 - 5 * (speed - 10))
+#    return 0.1 * r
+
+env = NRasterized(
+    agent=20,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+# %%
+model = PPO(
+    "CnnPolicy", env,
+    verbose=1,
+)
+model.learn(total_timesteps=100000)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)
+
+# %%

scratch/etienne/intersimple/ppo_speed_image_lowres.py

@@ -0,0 +1,49 @@
+# %%
+from stable_baselines3 import PPO
+from intersim.envs.intersimple import NRasterized, speed_reward
+import functools
+
+model_name = "ppo_speed_image_lowres"
+
+#def reward(state, action, info):
+#    speed = state[2].item()
+#    r = speed if speed < 10 else (10 - 5 * (speed - 10))
+#    return 0.1 * r
+
+env = NRasterized(
+    agent=51,
+    height=36,
+    width=36,
+    m_per_px=2,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+# %%
+model = PPO(
+    "CnnPolicy", env,
+    verbose=1,
+)
+model.learn(total_timesteps=100000)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)
+
+# %%

scratch/etienne/intersimple/ppo_speed_image_lowres_random.py

@@ -0,0 +1,42 @@
+# %%
+from stable_baselines3 import PPO
+from intersim.envs.intersimple import NRasterizedRandomAgent, speed_reward
+import functools
+
+model_name = "ppo_speed_image_lowres_random"
+
+env = NRasterizedRandomAgent(
+    height=36,
+    width=36,
+    m_per_px=2,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    )
+)
+
+# %%
+model = PPO(
+    "CnnPolicy", env,
+    verbose=1,
+    batch_size=2048,
+)
+model.learn(total_timesteps=2e5)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/ppo_speed_image_random.py

@@ -0,0 +1,39 @@
+# %%
+from stable_baselines3 import PPO
+from intersim.envs.intersimple import NRasterizedRandomAgent, speed_reward
+import functools
+
+model_name = "ppo_speed_image_random"
+
+env = NRasterizedRandomAgent(
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    )
+)
+
+# %%
+model = PPO(
+    "CnnPolicy", env,
+    verbose=1,
+    batch_size=2048,
+)
+model.learn(total_timesteps=2e5)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/ppo_speed_random.py

@@ -0,0 +1,43 @@
+# %%
+from stable_baselines3 import PPO
+from intersim.envs.intersimple import IntersimpleFlatRandomAgent, Reward, RewardVisualization, speed_reward
+import functools
+
+model_name = "ppo_speed_random"
+
+class IntersimpleRewardRandom(RewardVisualization, Reward, IntersimpleFlatRandomAgent):
+    """`IntersimpleFlatAgent` with rewards."""
+    pass
+
+env = IntersimpleRewardRandom(
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    )
+)
+
+# %%
+model = PPO(
+    "MlpPolicy", env,
+    verbose=1,
+    batch_size=2048,
+)
+model.learn(total_timesteps=2e5)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/ppo_tspeed.py

@@ -0,0 +1,39 @@
+# %%
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from intersim.envs.intersimple import IntersimpleTargetSpeedAgent
+
+model_name = "ppo_tspeed"
+
+env = IntersimpleTargetSpeedAgent(
+    agent=51,
+    target_speed=10,
+    speed_penalty_weight=0.001,
+    collision_penalty=1000
+)
+
+# %%
+model = PPO(
+    "MlpPolicy", env,
+    learning_rate=3e-6,
+    verbose=1,
+)
+model.learn(total_timesteps=2e5)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)

scratch/etienne/intersimple/ppo_tspeed_random.py

@@ -0,0 +1,31 @@
+# %%
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from intersim.envs.intersimple import IntersimpleTargetSpeedRandom
+
+model_name = "ppo_tspeed_random"
+
+# %%
+env = IntersimpleTargetSpeedRandom(target_speed=10)
+
+# %%
+model = PPO("MlpPolicy", env, verbose=1)
+model.learn(total_timesteps=250000)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = PPO.load(model_name)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close()

scratch/etienne/intersimple/render_env_from_model.py

@@ -0,0 +1,33 @@
+
+import stable_baselines3 as sb3
+from intersim.envs.intersimple import NRasterized
+
+
+def render_env(model_name='gail_image_multiagent_nocollision', agent=51, environment=NRasterized):
+    """
+    Render a video from an model, agent, and environment
+    Args:
+        model_name (str): name of the model
+        agent (int): agent to start the video from
+        environment (gym.Env): gym environment class to render environment on
+    """
+
+    model = sb3.PPO.load(model_name)
+
+    env = environment(stop_on_collision=False, width=36, height=36, m_per_px=2, agent=agent)
+
+    obs = env.reset()
+    i=0
+    while True and i < 600:
+        i+=1
+        action, _states = model.predict(obs)
+        obs, rewards, done, info = env.step(action)
+        env.render(mode='post')
+        if done:
+            break
+
+    env.close(filestr='render/'+model_name+'_agent%i'%(agent)) 
+
+if __name__ == '__main__':
+    import fire
+    fire.Fire(render_env)

scratch/etienne/intersimple/train_discrim.py

@@ -0,0 +1,71 @@
+# %%
+import sys
+sys.path.append('../../../')
+
+import pickle
+import imitation.data.rollout as rollout
+import imitation.data.types as types
+import torch
+from gail.envs import TLNRasterizedRouteRandomAgentLocation
+import tempfile
+import pathlib
+from imitation.util import logger
+from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv
+from imitation.algorithms import adversarial
+from src.discriminator import CnnDiscriminator
+import stable_baselines3
+from tqdm import tqdm
+
+with open("data/NormalizedIntersimpleExpertMu.001N50000_TLNRasterizedRouteRandomAgentLocationw70h70mppx1mu.001rskips50.pkl", "rb") as f:
+    trajectories = pickle.load(f)
+transitions = rollout.flatten_trajectories(trajectories)
+
+# %%
+env_settings = {'width': 70, 'height': 70, 'm_per_px': 1, 'mu': 0.001, 'random_skip': True, 'max_episode_steps': 200}
+env = TLNRasterizedRouteRandomAgentLocation(**env_settings)
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+logger.configure(tempdir_path / "GAIL/")
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+expert_batch_size = 4096
+
+venv = DummyVecEnv([lambda: env])
+discriminator = adversarial.GAIL(
+    expert_data=transitions,
+    expert_batch_size=expert_batch_size,
+    #discrim_kwargs={'discrim_net': NoisyDiscriminator(venv, std=0.25)},
+    disc_opt_cls=torch.optim.RMSprop,
+    disc_opt_kwargs={'lr': 0.0001, 'weight_decay': 0.003},
+    discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+    venv=venv, # unused
+    gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+)
+
+expert_data_loader = torch.utils.data.DataLoader(
+    transitions,
+    batch_size=expert_batch_size,
+    collate_fn=types.transitions_collate_fn,
+    shuffle=True,
+    drop_last=True,
+)
+
+gen_data_loader = torch.utils.data.DataLoader(
+    transitions,
+    batch_size=expert_batch_size,
+    collate_fn=types.transitions_collate_fn,
+    shuffle=True,
+    drop_last=True,
+)
+
+# %%
+epochs = 1000
+for i in tqdm(range(epochs)):
+    for expert_samples, gen_samples in zip(expert_data_loader, gen_data_loader):
+        # randomly corrupt actions
+        gen_samples['acts'] = -1 + 2 * torch.rand(*gen_samples['acts'].shape)
+
+        discriminator.train_disc(expert_samples=expert_samples, gen_samples=gen_samples)
+
+    torch.save(discriminator.discrim_net.state_dict(), 'train_discrim.pt')

scratch/etienne/pillbox/intersim_demos.ipynb

@@ -0,0 +1,249 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "%cd learners"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "/home/buehrle/dev/InteractionImitation/scratch/etienne/pillbox/learners\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "source": [
+    "import torch\n",
+    "import numpy as np"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# save expert demos to ../experts/Intersim/demos.npz\n",
+    "# make sure to split different experts up\n",
+    "\n",
+    "from intersim.envs.simulator import InteractionSimulator\n",
+    "from intersim.utils import get_map_path, get_svt, SVT_to_stateactions\n",
+    "import gym\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "def pillbox_demo(observations, actions, rewards):\n",
+    "    demo = {\n",
+    "        'env': 'intersim:intersim-v0',\n",
+    "        'num_trajs': len(observations),\n",
+    "        'mean_reward': rewards.mean(),\n",
+    "        'std_reward': rewards.std(),\n",
+    "    }\n",
+    "    demo.update({\n",
+    "        str(i): {\n",
+    "            'states': o,\n",
+    "            'actions': a,\n",
+    "        } for i, (o, a) in enumerate(zip(observations, actions))\n",
+    "    })\n",
+    "    return demo\n",
+    "\n",
+    "def intersim_expert_demos(loc, track):\n",
+    "    svt, svt_path = get_svt(loc, track)\n",
+    "    osm = get_map_path(loc)\n",
+    "    \n",
+    "    n_actors = svt.simstate.size(1)\n",
+    "    observations = []\n",
+    "    actions = [] ##\n",
+    "    #states, actions = SVT_to_stateactions(svt) ##\n",
+    "    rewards = []\n",
+    "    \n",
+    "    print('Simulating')\n",
+    "    env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm)\n",
+    "    obs, info = env.reset()\n",
+    "    for s in tqdm(svt.simstate[1:]): ##\n",
+    "    #for a in actions: ##\n",
+    "        relative_state = torch.stack((\n",
+    "            obs['relative_state'][..., 0],\n",
+    "            obs['relative_state'][..., 1],\n",
+    "            (obs['relative_state'][..., 2]**2 + obs['relative_state'][..., 3]**2).sqrt(),\n",
+    "            obs['relative_state'][..., 4],\n",
+    "            obs['relative_state'][..., 5],\n",
+    "        ), -1)\n",
+    "        observations.append(torch.cat((\n",
+    "            obs['state'].unsqueeze(1),\n",
+    "            relative_state,\n",
+    "        ), 1))\n",
+    "        obs, r, done, info = env.step(env.target_state(s, mu=.01))\n",
+    "        #obs, r, done, info = env.step(a) ##\n",
+    "        actions.append(info['action_taken'])\n",
+    "        rewards.append(r)\n",
+    "        assert not done, 'Episode terminated during expert demonstration.'\n",
+    "\n",
+    "    _except_idx = lambda o, i: torch.cat((o[:i], o[i+1:]))\n",
+    "        \n",
+    "    # transpose to per-agent observations and actions\n",
+    "    print('Transposing')\n",
+    "    observations = [torch.stack([_except_idx(o[i], i+1) for o in observations]) for i in range(n_actors)]\n",
+    "    actions = [torch.stack([a[i] for a in actions]) for i in range(n_actors)]\n",
+    "    \n",
+    "    print('Trimming')\n",
+    "    # trim observations and actions to start/end of trajectory\n",
+    "    _alive = lambda o: (~o.isnan().all(2).all(1)).nonzero()\n",
+    "    _start = lambda o: _alive(o).min()\n",
+    "    _end = lambda o: _alive(o).max() + 1\n",
+    "    start_end = [(_start(obs), _end(obs)) for obs in observations]\n",
+    "    observations = [obs[start:end] for obs, (start, end) in zip(observations, start_end)]\n",
+    "    actions = [act[start:end] for act, (start, end) in zip(actions, start_end)]\n",
+    "    \n",
+    "    #print('Cropping')\n",
+    "    ## crop observations to max number of observations\n",
+    "    #max_num_obs = max([(~obs.isnan().all(2)).sum(1).max() for obs in observations])\n",
+    "    #observations = [obs[:, :max_num_obs] for obs in observations]\n",
+    "    \n",
+    "    observations = [o.numpy() for o in observations]\n",
+    "    actions = [a.numpy() for a in actions]\n",
+    "    rewards = np.array(rewards)\n",
+    "    \n",
+    "    return pillbox_demo(observations, actions, rewards)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "demos = intersim_expert_demos(loc=0, track=0)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Simulating\n",
+      "Custom Vehicle Trajectory Paths\n",
+      "Map Path: datasets/maps/DR_USA_Roundabout_FT.osm\n",
+      "Environment Reset\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 3006/3006 [01:17<00:00, 38.87it/s]\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Transposing\n",
+      "Trimming\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": true,
+    "tags": [
+     "outputPrepend"
+    ]
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "demos['num_trajs']"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "151"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "demos['25']['states'].shape"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(71, 151, 5)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "np.savez('../experts/intersim:intersim-v0/demos.npz', **demos)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.5 64-bit ('.venv': venv)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  },
+  "interpreter": {
+   "hash": "56465d2ea10f338edb3d30adb010c5849fd826fffc543ba31360f3db8b47a703"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

scratch/etienne/pillbox/intersim_expert.ipynb

@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "%cd learners"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[Errno 2] No such file or directory: 'learners'\n",
+      "/home/buehrle/dev/InteractionImitation/scratch/etienne/pillbox/learners\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "import gym\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "def rollout(pi, max_steps=1000):\n",
+    "    env = gym.make('intersim:intersim-v0')\n",
+    "    env.reset() # obs = env.reset()\n",
+    "    obs, _, done, _ = env.step(0 * env.action_space.sample())\n",
+    "    \n",
+    "    _except = lambda o, i: torch.cat((o[:i], o[i+1:]))\n",
+    "    \n",
+    "    _relative_state_v = lambda obs: torch.stack((\n",
+    "        obs[..., 0],\n",
+    "        obs[..., 1],\n",
+    "        (obs[..., 2]**2 + obs[..., 3]**2).sqrt(),\n",
+    "        obs[..., 4],\n",
+    "        obs[..., 5],\n",
+    "    ), -1)\n",
+    "    \n",
+    "    for _ in tqdm(range(max_steps)):\n",
+    "        pi_obs = [\n",
+    "            torch.cat((e.unsqueeze(0), _relative_state_v(_except(o, i)))).unsqueeze(0)\n",
+    "            for i, (e, o) in enumerate(zip(obs['state'], obs['relative_state']))\n",
+    "        ]\n",
+    "        \n",
+    "        actions = [pi(o).squeeze() for o in pi_obs]\n",
+    "        actions = torch.stack(actions).unsqueeze(1)\n",
+    "        obs, _, done, _ = env.step(actions)\n",
+    "        env.render(mode='post')\n",
+    "        if done:\n",
+    "            break\n",
+    "    env.close()"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "\n",
+    "def expert(obs):\n",
+    "    ego = obs[:, 0]\n",
+    "    rel = obs[:, 1:]\n",
+    "    front = torch.stack((torch.cos(ego[:, 3]), torch.sin(ego[:, 3])), -1)\n",
+    "    left = torch.stack((-torch.sin(ego[:, 3]), torch.cos(ego[:, 3])), -1)\n",
+    "    df = (rel[:, :, :2] * front.unsqueeze(1)).sum(-1)\n",
+    "    dl = (rel[:, :, :2] * left.unsqueeze(1)).sum(-1)\n",
+    "\n",
+    "    df = torch.where(df.isnan(), np.inf * torch.ones_like(df), df)\n",
+    "    dl = torch.where(dl.isnan(), np.inf * torch.ones_like(dl), dl)\n",
+    "    rel = torch.where(rel.isnan(), np.inf * torch.ones_like(rel), rel)\n",
+    "\n",
+    "    # relative speed in direction of position difference vector\n",
+    "    vrel = rel[:, :, 2] * (rel[:, :, :2] * torch.stack((\n",
+    "        torch.cos(ego[:, 3].unsqueeze(1) + rel[:, :, 3]),\n",
+    "        torch.sin(ego[:, 3].unsqueeze(1) + rel[:, :, 3])),\n",
+    "        -1)).sum(-1)\n",
+    "    vrel = torch.where(vrel.isnan(), np.inf * torch.ones_like(vrel), vrel)\n",
+    "    vrel = torch.maximum(vrel, torch.zeros_like(vrel))\n",
+    "    \n",
+    "    alpha = torch.atan2(dl, df)\n",
+    "    d = (rel[:, :, :2] ** 2).sum(-1)\n",
+    "    attn = torch.exp(-torch.where(alpha > 0, 0.8*alpha, 1*alpha)**2 - 0.01 * d - 0.1*vrel) \n",
+    "    \n",
+    "    act = 10 - ego[:, 2] - 20 * attn.sum(-1)\n",
+    "    \n",
+    "    return act"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "rollout(expert, max_steps=500)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Vehicle Trajectory Paths: /home/buehrle/dev/InteractionImitation/InteractionSimulator/datasets/trackfiles/DR_USA_Roundabout_FT/vehicle_tracks_000.csv\n",
+      "Map Path: /home/buehrle/dev/InteractionImitation/InteractionSimulator/datasets/maps/DR_USA_Roundabout_FT.osm\n",
+      "Environment Reset\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "  0%|          | 0/500 [00:00<?, ?it/s]\n"
+     ]
+    },
+    {
+     "output_type": "error",
+     "ename": "RuntimeError",
+     "evalue": "torch.cat(): Sizes of tensors must match except in dimension 0. Got 5 and 6 in dimension 1 (The offending index is 1)",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_4266/633354333.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrollout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpert\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/tmp/ipykernel_4266/1839273282.py\u001b[0m in \u001b[0;36mrollout\u001b[0;34m(pi, max_steps)\u001b[0m\n\u001b[1;32m     12\u001b[0m         pi_obs = [\n\u001b[1;32m     13\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_except_self\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'state'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'relative_state'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         ]\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/tmp/ipykernel_4266/1839273282.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     12\u001b[0m         pi_obs = [\n\u001b[1;32m     13\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_except_self\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'state'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'relative_state'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         ]\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: torch.cat(): Sizes of tensors must match except in dimension 0. Got 5 and 6 in dimension 1 (The offending index is 1)"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.5 64-bit ('.venv': venv)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  },
+  "interpreter": {
+   "hash": "56465d2ea10f338edb3d30adb010c5849fd826fffc543ba31360f3db8b47a703"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

scratch/etienne/pillbox/learners/adril.py

@@ -0,0 +1,140 @@
+import gym
+from gym import spaces
+from sklearn.neighbors import KDTree
+from scipy.stats import norm
+import numpy as np
+import warnings
+from abc import ABC, abstractmethod
+from typing import Dict, Generator, Optional, Union
+import torch as th
+
+try:
+    # Check memory used by replay buffer when possible
+    import psutil
+except ImportError:
+    psutil = None
+
+from stable_baselines3.common.preprocessing import get_action_dim, get_obs_shape
+from stable_baselines3.common.type_aliases import ReplayBufferSamples, RolloutBufferSamples
+from stable_baselines3.common.vec_env import VecNormalize
+from stable_baselines3.common.buffers import ReplayBuffer
+
+
+class AdRILWrapper(gym.Env):
+  metadata = {'render.modes': ['human']}
+
+  def __init__(self, base_env):
+    super(AdRILWrapper, self).__init__()
+    self.base_env = base_env
+    self.iter = 0
+    self.observation_space = self.base_env.observation_space
+    self.action_space = self.base_env.action_space
+    self.trajs = list()
+    self.num_trajs = 0
+    self.curr_state = None
+  def step(self, action):
+    next_obs, _, done, info = self.base_env.step(action)
+    reward = self.iter # Transformed by replay buffer
+    self.trajs.append((self.curr_state, action, next_obs, done))
+    if done:
+        self.num_trajs += 1
+    self.curr_state = next_obs
+    return next_obs, reward, done, info
+  def reset(self):
+    obs = self.base_env.reset()
+    self.curr_state = obs
+    return obs
+  def render(self, mode='human'):
+    self.base_env.render(mode=mode)
+  def close (self):
+    self.base_env.close()
+  def get_learner_trajs(self):
+    return self.trajs
+  def set_iter(self, k):
+    self.iter = k
+    
+class AdRILReplayBuffer(ReplayBuffer):
+    def __init__(
+        self,
+        buffer_size: int,
+        observation_space: spaces.Space,
+        action_space: spaces.Space,
+        device: Union[th.device, str] = "cpu",
+        n_envs: int = 1,
+        optimize_memory_usage: bool = False,
+        expert_data: dict = dict(),
+            N_expert: int = 0,
+        balanced: bool = True,
+    ):
+        super(AdRILReplayBuffer, self).__init__(buffer_size, observation_space, action_space, device, n_envs=n_envs, optimize_memory_usage=optimize_memory_usage)
+
+        self.expert_states = expert_data['obs']
+        self.expert_actions = expert_data['acts']
+        self.expert_next_states = expert_data['next_obs']
+        self.expert_dones = expert_data['dones']
+        n_expert = len(expert_data["obs"])
+        self.iter = 0
+        self.N_expert = N_expert
+        self.N_learner = 0
+        self.normalizer = 1
+        self.balanced = balanced
+
+    def set_iter(self, k):
+      self.iter = k
+      normalizer = 0
+      for i in range(0, k):
+        normalizer += 1 ** (-i) # written to support decaying learning rate
+      self.normalizer = normalizer
+
+    def set_n_learner(self, n):
+        self.N_learner = n
+
+    def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> ReplayBufferSamples:
+        num_samples = len(batch_inds)
+        if self.balanced:
+          num_expert_samples = int(num_samples / 2)
+          batch_inds = batch_inds[:num_expert_samples]
+          expert_inds = np.random.randint(0, len(self.expert_states), size=num_expert_samples)
+          # balanced sampling
+          if self.optimize_memory_usage:
+              next_obs = self._normalize_obs(self.observations[(batch_inds + 1) % self.buffer_size, 0, :], env)
+          else:
+              next_obs = self._normalize_obs(self.next_observations[batch_inds, 0, :], env)
+          next_obs = np.concatenate((next_obs, self._normalize_obs(self.expert_next_states[expert_inds], env)), axis=0)
+          obs = self._normalize_obs(self.observations[batch_inds, 0, :], env)
+          obs = np.concatenate((obs, self._normalize_obs(self.expert_states[expert_inds], env)), axis=0)
+          actions = self.actions[batch_inds, 0, :]
+          actions = np.concatenate((actions, self.expert_actions[expert_inds].reshape(num_expert_samples, -1)), axis=0)
+          dones = self.dones[batch_inds]
+          dones = np.concatenate((dones, self.expert_dones[expert_inds].reshape(num_expert_samples, -1)), axis=0)
+          # AdRIL Rewards (indicator kernel)
+          mask1 = (self.rewards[batch_inds] >= 0).astype(np.float32)
+          mask2 = (self.rewards[batch_inds] < self.iter).astype(np.float32)
+          r1 = - (1. ** (-self.rewards[batch_inds])) * mask1 * mask2 # Past iter
+          r2 = np.zeros_like(self.rewards[batch_inds]) * mask1 * (1 - mask2) # current iter
+          r3 = -self.rewards[batch_inds] * (1 - mask1) # Expert
+          if self.iter > 0:
+              rewards = (r1 / self.N_learner) + r2 + r3
+          else:
+              rewards = r1 + r2 + r3
+          rewards = np.concatenate((rewards, np.ones_like(rewards) / self.N_expert), axis=0)
+        else:
+          if self.optimize_memory_usage:
+              next_obs = self._normalize_obs(self.observations[(batch_inds + 1) % self.buffer_size, 0, :], env)
+          else:
+              next_obs = self._normalize_obs(self.next_observations[batch_inds, 0, :], env)
+          obs = self._normalize_obs(self.observations[batch_inds, 0, :], env)
+          actions = self.actions[batch_inds, 0, :]
+          dones = self.dones[batch_inds]
+          # AdRIL Rewards (indicator kernel)
+          mask1 = (self.rewards[batch_inds] >= 0).astype(np.float32)
+          mask2 = (self.rewards[batch_inds] < self.iter).astype(np.float32)
+          r1 = - (1. ** (-self.rewards[batch_inds])) * mask1 * mask2 # Past iter
+          r2 = np.zeros_like(self.rewards[batch_inds]) * mask1 * (1 - mask2) # current iter
+          r3 = -self.rewards[batch_inds] * (1 - mask1) / self.N_expert # Expert
+          if self.iter > 0:
+              rewards = (r1 * 1. / self.N_learner) + r2 + r3
+          else:
+              rewards = r1 + r2 + r3
+        data = (obs, actions, next_obs, dones, rewards)
+        return ReplayBufferSamples(*tuple(map(self.to_torch, data)))

scratch/etienne/pillbox/learners/advil.py

@@ -0,0 +1,222 @@
+import numpy as np
+
+import torch
+import torch.autograd as autograd
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from gym.spaces import Discrete
+import gym
+from stable_baselines3.common.preprocessing import get_action_dim
+from tqdm import tqdm
+from torch.autograd import Variable
+from itertools import repeat
+from torch.autograd import grad as torch_grad
+from typing import List, Type
+import types
+
+# Infinite dataloader
+def repeater(data_loader):
+    for loader in repeat(data_loader):
+        for data in loader:
+            yield data
+
+def create_mlp(
+    input_dim: int, output_dim: int, net_arch: List[int], activation_fn: Type[nn.Module] = nn.ReLU) -> List[nn.Module]:
+
+    if len(net_arch) > 0:
+        modules = [nn.Linear(input_dim, net_arch[0]), activation_fn()]
+    else:
+        modules = []
+
+    for idx in range(len(net_arch) - 1):
+        modules.append(nn.Linear(net_arch[idx], net_arch[idx + 1]))
+        modules.append(activation_fn())
+
+    if output_dim > 0:
+        last_layer_dim = net_arch[-1] if len(net_arch) > 0 else input_dim
+        modules.append(nn.Linear(last_layer_dim, output_dim))
+    return modules
+
+def init_ortho(layer):
+    if type(layer) == nn.Linear:
+        nn.init.orthogonal_(layer.weight)
+
+        
+class AdVILPolicy(nn.Module):
+    def __init__(self, env, mean=None, std=None):
+        super(AdVILPolicy, self).__init__()
+        if isinstance(env.action_space, Discrete):
+            self.net_arch = [64, 64]
+            self.action_dim = env.action_space.n
+            self.discrete = True
+        else:
+            self.net_arch = [256, 256]
+            self.action_dim = int(np.prod(env.action_space.shape))
+            self.low = torch.as_tensor(env.action_space.low)
+            self.high = torch.as_tensor(env.action_space.high)
+            self.discrete = False
+        self.obs_dim = int(np.prod(env.observation_space.shape))
+        self.observation_space = env.observation_space
+        net = create_mlp(self.obs_dim, self.action_dim, self.net_arch, nn.ReLU)
+        if self.discrete:
+            net.append(nn.Softmax(dim=1))
+        self.net = nn.Sequential(*net)
+        self.net.apply(init_ortho)
+        if mean is not None and std is not None:
+            self.mean = mean
+            self.std = std
+            self.is_normalized = True
+        else:
+            self.is_normalized = False
+    def forward(self, obs):
+        action = self.net(obs)
+        return action
+    def predict(self, obs, state, mask, deterministic):
+        obs = obs.reshape((-1,) + (self.obs_dim,))
+        if self.is_normalized:
+            obs = (obs - self.mean) / self.std
+        obs = torch.as_tensor(obs)
+        with torch.no_grad():
+            actions = self.forward(obs)
+            if self.discrete:
+                actions = actions.argmax(dim=1).reshape(-1)
+            else:
+                actions = self.low + ((actions + 1.0) / 2.0) * (self.high - self.low)
+                actions = torch.max(torch.min(actions, self.high), self.low)
+            actions = actions.cpu().numpy()
+        return actions, state
+
+
+class AdVILDiscriminator(nn.Module):
+    def __init__(self, env):
+        super(AdVILDiscriminator, self).__init__()
+        if isinstance(env.action_space, Discrete):
+            self.net_arch = [64, 64]
+            self.action_dim = env.action_space.n
+        else:
+            self.net_arch = [256, 256]
+            self.action_dim = int(np.prod(env.action_space.shape))
+        self.obs_dim = int(np.prod(env.observation_space.shape))
+        net = create_mlp(self.obs_dim + self.action_dim, 1, self.net_arch, nn.ReLU)
+        self.net = nn.Sequential(*net)
+        self.net.apply(init_ortho)
+
+    def forward(self, inputs):
+        output = self.net(inputs)
+        return output.view(-1)
+
+def pi_update(obs, acts, pi, f, pi_opt, prog):
+    pi_opt.zero_grad()
+    obs_v = Variable(obs)
+    pi_acts = pi(obs_v)
+    #learner_sa = torch.cat((obs, pi_acts), axis=1)
+    f_learner = f(obs, acts)
+    pi_loss = f_learner.mean() + orthogonal_reg(pi) + 2e-1 * (pi_acts - acts).square().mean()
+    pi_loss.backward()
+    if prog > 0.1:
+        torch.nn.utils.clip_grad_norm(pi.parameters(), 40.0)
+    pi_opt.step()
+    return pi_loss.item(), (2e-1 * (pi_acts - acts).square().mean()).item()
+
+def orthogonal_reg(pi):
+    with torch.enable_grad():
+        reg = 1e-4
+        orth_loss = torch.zeros(1)
+        for name, param in pi.named_parameters():
+            if 'bias' not in name:
+                x = torch.mm(torch.t(param), param)
+                x = x * (1. - torch.eye(param.shape[-1]))
+                orth_loss = orth_loss + reg * (x.square().sum())
+        return orth_loss
+
+def f_update(obs, acts, pi, f, f_opt, prog):
+    obs_v = Variable(obs)
+    pi_acts = pi(obs_v)
+    #learner_sa = torch.cat((obs, pi_acts), axis=1)
+    #expert_sa = Variable(torch.cat((obs, acts), axis=1))
+    f_learner = f(obs, pi_acts)
+    f_expert = f(obs, acts)
+    #gp = gradient_penalty((obs, pi_acts), (obs, acts), f)
+    f_opt.zero_grad()
+    f_loss = f_expert.mean() - f_learner.mean()# + 10 * gp
+    f_loss.backward()
+    if prog > 0.1:
+        torch.nn.utils.clip_grad_norm(f.parameters(), 40.0)
+    f_opt.step()
+    return f_loss.item()
+
+def gradient_penalty(learner_sa, expert_sa, f):
+    batch_size = expert_sa[0].size()[0]
+
+    #alpha = torch.rand(batch_size, 1)
+    #alpha = alpha.expand_as(expert_sa)
+
+    salpha = torch.rand(batch_size, 1, 1)
+    salpha = salpha.expand_as(expert_sa[0])
+
+    aalpha = torch.rand(batch_size, 1)
+    aalpha = aalpha.expand_as(expert_sa[1])
+
+    #interpolated = alpha * expert_sa.data + (1 - alpha) * learner_sa.data
+    #interpolated = Variable(interpolated, requires_grad=True)
+    #f_interpolated = f(interpolated.float())
+
+    sinterpolated = salpha * expert_sa[0].data + (1 - salpha) * learner_sa[0].data
+    sinterpolated = Variable(sinterpolated, requires_grad=True)
+
+    ainterpolated = aalpha * expert_sa[1].data + (1 - aalpha) * learner_sa[1].data
+    ainterpolated = Variable(ainterpolated, requires_grad=True)
+    
+    f_interpolated = f(sinterpolated, ainterpolated)
+
+    #gradients = torch_grad(outputs=f_interpolated, inputs=interpolated,
+    #                       grad_outputs=torch.ones(f_interpolated.size()),
+    #                       create_graph=True, retain_graph=True)[0]
+    
+    sgradients = torch_grad(outputs=f_interpolated, inputs=sinterpolated,
+                           grad_outputs=torch.ones(f_interpolated.size()),
+                           create_graph=True, retain_graph=True)[0]
+
+    agradients = torch_grad(outputs=f_interpolated, inputs=ainterpolated,
+                           grad_outputs=torch.ones(f_interpolated.size()),
+                           create_graph=True, retain_graph=True)[0]
+
+    #gradients = gradients.view(batch_size, -1)
+    sgradients = sgradients.view(batch_size, -1)
+    agradients = agradients.view(batch_size, -1)
+    #norm = gradients.norm(2, dim=1).mean().item()
+    #gradients_norm = torch.sqrt(torch.sum(gradients ** 2, dim=1) + 1e-12)
+    gradients_norm = torch.sqrt(torch.sum(sgradients ** 2, dim=1) + torch.sum(agradients ** 2, dim=1) + 1e-12)
+    # 2 * |f'(x_0)|
+    return ((gradients_norm - 0.4) ** 2).mean()
+
+def advil_training(data_loader, env, iters=int(1e5), policy_class=AdVILPolicy, discriminator_class=AdVILDiscriminator, lr_pi=8e-6, lr_f=8e-4):
+    if not isinstance(env.action_space, Discrete):
+        low = torch.as_tensor(env.action_space.low)
+        high = torch.as_tensor(env.action_space.high)
+    if data_loader.dataset.is_normalized:
+        pi = policy_class(env, data_loader.dataset.mean, data_loader.dataset.std)
+    else:
+        pi = policy_class(env)
+    f = discriminator_class(env)
+    pi_opt = optim.Adam(pi.parameters(), lr=lr_pi)
+
+    last_loss = 0
+    f_opt = optim.Adam(f.parameters(), lr=lr_f)
+    data_loader = repeater(data_loader)
+    for t in tqdm(range(iters)):
+        data = next(data_loader)
+        obs = data['obs']
+        acts = data['acts']
+        #if isinstance(env.action_space, Discrete):
+        #    acts = nn.functional.one_hot(acts, env.action_space.n)
+        #else:
+        #    acts = (((acts - low) / (high - low)) * 2.0) - 1.0
+        pi_loss, mse_reg = pi_update(obs, acts, pi, f, pi_opt, t/iters)
+        f_loss = f_update(obs, acts, pi, f, f_opt, t/iters)
+        if t % 100 == 0:
+            print("pi loss:", pi_loss)
+            print("mse reg:", mse_reg)
+            print("f loss:", f_loss)
+    return pi

scratch/etienne/pillbox/learners/intersim_advil.py

@@ -0,0 +1,155 @@
+import torch
+import torch.nn as nn
+
+def unnormalize(val, mean, std):
+    val *= std or 1
+    val += mean or 0
+    return val
+
+def normalize(val, mean, std):
+    val -= mean or 0
+    val /= std or 1
+    return val
+
+class IntersimPolicy(nn.Module):
+    def __init__(self, env, mean=None, std=None):
+        # assert "intersim" in env.unwrapped.spec.id
+        super().__init__()
+
+        self._ego_encoder = nn.Sequential(
+            # in 5, out 5
+            nn.Linear(5, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 5),
+            nn.ReLU(),
+        )
+        self._state_encoder = nn.Sequential(
+            # in 5, out 5
+            nn.Linear(5, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 5),
+            nn.ReLU(),
+        )
+        self._deepset = lambda e: e.sum(-2)
+        self._action_decoder = nn.Sequential(
+            # in 5 + 5, out 1
+            nn.Linear(5 + 5, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 1),
+        )
+    
+    def forward(self, obs):
+        # obs.shape = (batch=514, 1 + others=150, 5)
+        # act.shape = (batch=514, 1)
+        
+        ego = obs[:, 0]#.detach().clone()
+        rel = obs[:, 1:]#.detach().clone()
+        nan = rel.isnan().any(-1, keepdim=True)
+        rel = torch.where(nan, torch.zeros_like(rel), rel) # required because of https://github.com/pytorch/pytorch/issues/15506
+        
+        d = (rel[:, :, :2] ** 2).sum(-1).sqrt()
+        front = torch.stack((torch.cos(ego[:, 3]), torch.sin(ego[:, 3])), -1)
+        left = torch.stack((-torch.sin(ego[:, 3]), torch.cos(ego[:, 3])), -1)
+        df = (rel[:, :, :2] * front.unsqueeze(1)).sum(-1)
+        dl = (rel[:, :, :2] * left.unsqueeze(1)).sum(-1)
+        alpha = torch.atan2(dl, df)
+        
+        rel[:, :, 0] = d
+        rel[:, :, 1] = alpha
+
+        e = self._ego_encoder(ego)
+        x = self._state_encoder(rel)
+        x = torch.where(nan, torch.zeros_like(x), x)
+        x = self._deepset(x)
+        a = self._action_decoder(torch.cat((e, x), 1))
+        
+        return 10 * a
+
+    def predict(self, state, mask, deterministic):
+        #action_distribution = self.forward(obs)
+        #action = action_distribution.argmax()
+        #return action
+        return self.forward(obs)
+
+class IntersimDiscriminator(nn.Module):
+    def __init__(self, env):
+        # assert "intersim" in env.unwrapped.spec.id
+        super().__init__()
+        
+        self._ego_encoder = nn.Sequential(
+            # in 5, out 5
+            nn.Linear(5, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 5),
+            nn.ReLU(),
+        )
+        self._state_encoder = nn.Sequential(
+            # in 5, out 5
+            nn.Linear(5, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 5),
+            nn.ReLU(),
+        )
+        self._deepset = lambda e: e.sum(-2)
+        self._discriminator = nn.Sequential(
+            # in 5 + 5 + 1, out 1
+            nn.Linear(5 + 5 + 1, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 1),
+        )
+    
+    def forward(self, obs, acts):
+        # obs.shape = (batch=514, 1 + others=150, 5)
+        # acts.shape = (batch=514, 1)
+        # val.shape = (batch=514,)
+
+        ego = obs[:, 0]
+        rel = obs[:, 1:]
+        nan = rel.isnan().any(-1, keepdim=True)
+        rel = torch.where(nan, torch.zeros_like(rel), rel) # required because of https://github.com/pytorch/pytorch/issues/15506
+        
+        d = (rel[:, :, :2] ** 2).sum(-1).sqrt()
+        front = torch.stack((torch.cos(ego[:, 3]), torch.sin(ego[:, 3])), -1)
+        left = torch.stack((-torch.sin(ego[:, 3]), torch.cos(ego[:, 3])), -1)
+        df = (rel[:, :, :2] * front.unsqueeze(1)).sum(-1)
+        dl = (rel[:, :, :2] * left.unsqueeze(1)).sum(-1)
+        alpha = torch.atan2(dl, df)
+        
+        rel[:, :, 0] = d
+        rel[:, :, 1] = alpha
+        
+        e = self._ego_encoder(ego)
+        x = self._state_encoder(rel)
+        x = torch.where(nan, torch.zeros_like(x), x)
+        x = self._deepset(x)
+        v = self._discriminator(torch.cat((e, x, acts), 1))
+        
+        return v.squeeze(1)

scratch/etienne/pillbox/learners/soft_q.py

@@ -0,0 +1,31 @@
+from typing import Any, Dict, List, Optional, Type
+
+import gym
+import torch as th
+from torch import nn
+
+from stable_baselines3.common.policies import BasePolicy, register_policy
+from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
+from stable_baselines3.dqn.policies import DQNPolicy, QNetwork
+
+
+class SoftQNetwork(QNetwork):
+    def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
+        q_values = self.forward(observation)
+        probs = nn.functional.softmax(q_values * 10, dim=1)
+        m = th.distributions.Categorical(probs)
+        action = m.sample().reshape(-1) 
+        return action
+
+
+class SQLPolicy(DQNPolicy):
+    def make_q_net(self) -> SoftQNetwork:
+        # Make sure we always have separate networks for features extractors etc
+        net_args = self._update_features_extractor(
+            self.net_args, features_extractor=None)
+        return SoftQNetwork(**net_args).to(self.device)
+
+
+SoftMlpPolicy = SQLPolicy
+
+register_policy("SoftMlpPolicy", SoftMlpPolicy)

scratch/etienne/pillbox/learners/sqil.py

@@ -0,0 +1,61 @@
+import warnings
+from abc import ABC, abstractmethod
+from typing import Dict, Generator, Optional, Union
+
+import numpy as np
+import torch as th
+from gym import spaces
+
+try:
+    # Check memory used by replay buffer when possible
+    import psutil
+except ImportError:
+    psutil = None
+
+from stable_baselines3.common.preprocessing import get_action_dim, get_obs_shape
+from stable_baselines3.common.type_aliases import ReplayBufferSamples, RolloutBufferSamples
+from stable_baselines3.common.vec_env import VecNormalize
+from stable_baselines3.common.buffers import ReplayBuffer
+
+
+class SQILReplayBuffer(ReplayBuffer):
+    def __init__(
+        self,
+        buffer_size: int,
+        observation_space: spaces.Space,
+        action_space: spaces.Space,
+        device: Union[th.device, str] = "cpu",
+        n_envs: int = 1,
+        optimize_memory_usage: bool = False,
+        expert_data: dict = dict(),
+    ):
+        super(SQILReplayBuffer, self).__init__(buffer_size, observation_space, action_space, device, n_envs=n_envs, optimize_memory_usage=optimize_memory_usage)
+
+        self.expert_states = expert_data['obs']
+        self.expert_actions = expert_data['acts']
+        self.expert_next_states = expert_data['next_obs']
+        self.expert_dones = expert_data['dones']
+
+    def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> ReplayBufferSamples:
+        num_samples = len(batch_inds)
+        num_expert_samples = int(num_samples / 2)
+        batch_inds = batch_inds[:num_expert_samples]
+        expert_inds = np.random.randint(0, len(self.expert_states), size=num_expert_samples)
+        # Balanced sampling
+        if self.optimize_memory_usage:
+            next_obs = self._normalize_obs(self.observations[(batch_inds + 1) % self.buffer_size, 0, :], env)
+        else:
+            next_obs = self._normalize_obs(self.next_observations[batch_inds, 0, :], env)
+        next_obs = np.concatenate((next_obs, self._normalize_obs(self.expert_next_states[expert_inds], env)), axis=0)
+        obs = self._normalize_obs(self.observations[batch_inds, 0, :], env)
+        obs = np.concatenate((obs, self._normalize_obs(self.expert_states[expert_inds], env)), axis=0)
+        actions = self.actions[batch_inds, 0, :]
+        actions = np.concatenate((actions, self.expert_actions[expert_inds].reshape(num_expert_samples, -1)), axis=0)        
+        dones = self.dones[batch_inds]
+        dones = np.concatenate((dones, self.expert_dones[expert_inds].reshape(num_expert_samples, -1)), axis=0)
+        # SQIL Rewards
+        rewards = self.rewards[batch_inds] * 0.
+        rewards = np.concatenate((rewards, np.ones_like(rewards)), axis=0)
+
+        data = (obs, actions, next_obs, dones, rewards)
+        return ReplayBufferSamples(*tuple(map(self.to_torch, data)))

scratch/etienne/pillbox/learners/train.py

@@ -0,0 +1,248 @@
+from imitation.algorithms import adversarial, bc
+from imitation.util import logger, util
+from stable_baselines3 import PPO, DQN, SAC
+from soft_q import SQLPolicy
+from sqil import SQILReplayBuffer
+from stable_baselines3.common import policies
+from stable_baselines3.common.evaluation import evaluate_policy
+from imitation.rewards import discrim_nets
+import numpy as np
+import argparse
+from utils import make_sa_dataloader, make_sads_dataloader, make_sa_dataset, linear_schedule
+from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
+from adril import AdRILWrapper, AdRILReplayBuffer
+import os
+from gym.spaces import Discrete
+import gym
+from advil import advil_training
+from stable_baselines3.common.running_mean_std import RunningMeanStd
+
+from advil import AdVILPolicy, AdVILDiscriminator
+
+def train_bc(env, n=0):
+    venv = util.make_vec_env(env, n_envs=8)
+    if isinstance(venv.action_space, Discrete):
+        w = 64
+    else:
+        w = 256
+    for i in range(n):
+        mean_rewards = []
+        std_rewards = []
+        for num_trajs in range(0, 26, 5):
+            if num_trajs == 0:
+                expert_data = make_sa_dataloader(env, normalize=False)
+            else:
+                expert_data = make_sa_dataloader(env, max_trajs=num_trajs, normalize=False)
+            bc_trainer = bc.BC(venv.observation_space, venv.action_space, expert_data=expert_data,
+                               policy_class=policies.ActorCriticPolicy,
+                               ent_weight=0., l2_weight=0., policy_kwargs=dict(net_arch=[w, w]))
+            if num_trajs > 0:
+                bc_trainer.train(n_batches=int(5e5))
+
+            def get_policy(*args, **kwargs):
+                return bc_trainer.policy
+            model = PPO(get_policy, env, verbose=1)
+            model.save(os.path.join("learners", env,
+                                    "bc_{0}_{1}".format(i, num_trajs)))
+            mean_reward, std_reward = evaluate_policy(
+                    model, model.get_env(), n_eval_episodes=10)               
+            mean_rewards.append(mean_reward)
+            std_rewards.append(std_reward)
+            print("{0} Trajs: {1}".format(num_trajs, mean_reward))
+            np.savez(os.path.join("learners", env, "bc_rewards_{0}".format(
+                i)), means=mean_rewards, stds=std_rewards)
+
+
+def train_gail(env, n=0):
+    venv = util.make_vec_env(env, n_envs=8)
+    if isinstance(venv.action_space, Discrete):
+        w = 64
+    else:
+        w = 256
+    expert_data = make_sads_dataloader(env, max_trajs=5)
+    logger.configure(os.path.join("learners", "GAIL"))
+
+    for i in range(n):
+        discrim_net = discrim_nets.ActObsMLP(
+                action_space=venv.action_space,
+                observation_space=venv.observation_space,
+                hid_sizes=(w, w),
+                )
+        gail_trainer = adversarial.GAIL(venv, expert_data=expert_data, expert_batch_size=32,
+                                        gen_algo=PPO("MlpPolicy", venv, verbose=1, n_steps=1024,
+                                                     policy_kwargs=dict(net_arch=[w, w])),
+                                                     discrim_kwargs={'discrim_net': discrim_net})
+        mean_rewards = []
+        std_rewards = []
+        for train_steps in range(20):
+            if train_steps > 0:
+                if 'Bullet' in env:
+                    gail_trainer.train(total_timesteps=25000)
+                else:
+                    gail_trainer.train(total_timesteps=16384)
+
+            def get_policy(*args, **kwargs):
+                return gail_trainer.gen_algo.policy
+            model = PPO(get_policy, env, verbose=1)
+            mean_reward, std_reward = evaluate_policy(
+                model, model.env, n_eval_episodes=10)
+            mean_rewards.append(mean_reward)
+            std_rewards.append(std_reward)
+            print("{0} Steps: {1}".format(train_steps, mean_reward))
+            np.savez(os.path.join("learners", env, "gail_rewards_{0}".format(i)),
+                     means=mean_rewards, stds=std_rewards)
+
+
+def train_sqil(env, n=0):
+    venv = gym.make(env)
+    expert_data = make_sa_dataset(env, max_trajs=5)
+
+    for i in range(n):
+        if isinstance(venv.action_space, Discrete):
+            model = DQN(SQLPolicy, venv, verbose=1, policy_kwargs=dict(net_arch=[64, 64]), learning_starts=1)
+        else:
+            model = SAC('MlpPolicy', venv, verbose=1, policy_kwargs=dict(net_arch=[256, 256]), ent_coef='auto',
+                        learning_rate=linear_schedule(7.3e-4), train_freq=64, gradient_steps=64, gamma=0.98, tau=0.02)
+
+        model.replay_buffer = SQILReplayBuffer(model.buffer_size, model.observation_space,
+                                               model.action_space, model.device, 1,
+                                               model.optimize_memory_usage, expert_data=expert_data)
+        mean_rewards = []
+        std_rewards = []
+        for train_steps in range(20):
+            if train_steps > 0:
+                if 'Bullet' in env:
+                    model.learn(total_timesteps=25000, log_interval=1)
+                else:
+                    model.learn(total_timesteps=16384, log_interval=1)
+            mean_reward, std_reward = evaluate_policy(
+                model, model.env, n_eval_episodes=10)
+            mean_rewards.append(mean_reward)
+            std_rewards.append(std_reward)
+            print("{0} Steps: {1}".format(train_steps, mean_reward))
+            np.savez(os.path.join("learners", env, "sqil_rewards_{0}".format(i)),
+                     means=mean_rewards, stds=std_rewards)
+
+
+def train_adril(env, n=0, balanced=False):
+    num_trajs = 20
+    expert_data = make_sa_dataset(env, max_trajs=num_trajs)
+    n_expert = len(expert_data["obs"])
+    expert_sa = np.concatenate((expert_data["obs"], np.reshape(expert_data["acts"], (n_expert, -1))), axis=1)
+
+    for i in range(0, n):
+        venv = AdRILWrapper(gym.make(env))
+        mean_rewards = []
+        std_rewards = []
+        # Create model
+        if isinstance(venv.action_space, Discrete):
+            model = DQN(SQLPolicy, venv, verbose=1, policy_kwargs=dict(net_arch=[64, 64]), learning_starts=1)
+        else:
+            model = SAC('MlpPolicy', venv, verbose=1, policy_kwargs=dict(net_arch=[256, 256]), ent_coef='auto',
+                        learning_rate=linear_schedule(7.3e-4), train_freq=64, gradient_steps=64, gamma=0.98, tau=0.02)
+        model.replay_buffer = AdRILReplayBuffer(model.buffer_size, model.observation_space,
+                                               model.action_space, model.device, 1,
+                                               model.optimize_memory_usage, expert_data=expert_data, N_expert=num_trajs,
+                                               balanced=balanced)
+        if not balanced:
+            for j in range(len(expert_sa)):
+                obs = expert_data["obs"][j]
+                act = expert_data["acts"][j]
+                next_obs = expert_data["next_obs"][j]
+                done = expert_data["dones"][j]
+                model.replay_buffer.add(obs, next_obs, act, -1, done)    
+        for train_steps in range(400):
+            # Train policy
+            if train_steps > 0:
+                if 'Bullet' in env:
+                    model.learn(total_timesteps=1250, log_interval=1000)
+                else:
+                    model.learn(total_timesteps=25000, log_interval=1000)
+                if train_steps % 1 == 0: # written to support more complex update schemes
+                    model.replay_buffer.set_iter(train_steps)
+                    model.replay_buffer.set_n_learner(venv.num_trajs)
+            
+            # Evaluate policy
+            if train_steps % 20 == 0:
+                model.set_env(gym.make(env))
+                mean_reward, std_reward = evaluate_policy(
+                    model, model.env, n_eval_episodes=10)
+                mean_rewards.append(mean_reward)
+                std_rewards.append(std_reward)
+                print("{0} Steps: {1}".format(int(train_steps * 1250), mean_reward))
+                np.savez(os.path.join("learners", env, "adril_rewards_{0}".format(i)),
+                        means=mean_rewards, stds=std_rewards)
+            # Update env
+            if train_steps > 0:
+                if train_steps % 1  == 0:
+                    venv.set_iter(train_steps + 1)
+            model.set_env(venv)
+
+
+def train_advil(env, policy_class=AdVILPolicy, discriminator_class=AdVILDiscriminator,
+                iters=int(1e5), lr_pi=8e-6, lr_f=8e-4):
+    venv = gym.make(env)
+    expert_data = make_sa_dataloader(
+        env,
+        normalize=False,
+        batch_size=1024,
+    )
+    pi = advil_training(
+        expert_data,
+        venv,
+        iters=iters,
+        policy_class=policy_class,
+        discriminator_class=discriminator_class, 
+        lr_pi=lr_pi,
+        lr_f=lr_f,
+    )
+    return pi
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Train expert policies.')
+    parser.add_argument(
+        '-a', '--algo', choices=['bc', 'gail', 'sqil', 'adril', 'advil', 'all'], required=True)
+    parser.add_argument('-e', '--env', choices=['cartpole', 'lunarlander', 'acrobot', 'pendulum', 'halfcheetah', 'walker', 'hopper', 'ant'],
+                        required=True)
+    parser.add_argument('-n', '--num_runs', required=False)
+    args = parser.parse_args()
+    if args.env == "cartpole":
+        envname = 'CartPole-v1'
+    elif args.env == "lunarlander":
+        envname = 'LunarLander-v2'
+    elif args.env == "acrobot":
+        envname = 'Acrobot-v1'
+    elif args.env == "pendulum":
+        envname = 'Pendulum-v0'
+    elif args.env == "halfcheetah":
+        envname = 'HalfCheetahBulletEnv-v0'
+    elif args.env == "walker":
+        envname = 'Walker2DBulletEnv-v0'
+    elif args.env == "hopper":
+        envname = 'HopperBulletEnv-v0'
+    elif args.env == "ant":
+        envname = 'AntBulletEnv-v0'
+    else:
+        print("ERROR: unsupported env.")
+    if args.num_runs is not None and args.num_runs.isdigit():
+        num_runs = int(args.num_runs)
+    else:
+        num_runs = 1
+    if args.algo == 'bc':
+        train_bc(envname, num_runs)
+    elif args.algo == 'gail':
+        train_gail(envname, num_runs)
+    elif args.algo == 'sqil':
+        train_sqil(envname, num_runs)
+    elif args.algo == 'adril':
+        train_adril(envname, num_runs)
+    elif args.algo == 'advil':
+        train_advil(envname, num_runs)
+    elif args.algo == 'all':
+        train_bc(envname, num_runs)
+        train_gail(envname, num_runs)
+        train_sqil(envname, num_runs)
+        train_adril(envname, num_runs)
+        train_advil(envname, num_runs)
+    else:
+        print("ERROR: unsupported algorithm")

scratch/etienne/pillbox/learners/utils.py

@@ -0,0 +1,129 @@
+import numpy as np
+import torch
+from torch.utils.data import Dataset, DataLoader
+from itertools import chain
+from typing import Callable, Union, Type, Optional, Dict, Any
+
+# From https://github.com/DLR-RM/rl-baselines3-zoo/blob/8ea4f4a87afa548832ca17e575b351ec5928c1b0/utils/utils.py
+def linear_schedule(initial_value: Union[float, str]) -> Callable[[float], float]:
+    """
+    Linear learning rate schedule.
+    :param initial_value: (float or str)
+    :return: (function)
+    """
+    if isinstance(initial_value, str):
+        initial_value = float(initial_value)
+
+    def func(progress_remaining: float) -> float:
+        """
+        Progress will decrease from 1 (beginning) to 0
+        :param progress_remaining: (float)
+        :return: (float)
+        """
+        return progress_remaining * initial_value
+
+    return func
+
+class SADataset(torch.utils.data.Dataset):
+    def __init__(self, obs, acts, normalize):
+        if normalize:
+            obs = np.array(obs)
+            self.mean = obs.mean(axis=0)
+            self.std = obs.std(axis=0) + 1e-3
+            obs = (obs - self.mean) / (self.std)
+            self.is_normalized = True
+        else:
+            self.is_normalized = False
+        self.obs = torch.tensor(obs)
+        self.acts = torch.tensor(acts)
+
+    def __len__(self):
+        return len(self.obs)
+
+    def __getitem__(self, idx):
+        if torch.is_tensor(idx):
+            idx = idx.tolist()
+        obs = self.obs[idx]
+        acts = self.acts[idx]
+        sample = {'obs': obs, 'acts': acts}
+        return sample
+
+def make_sa_dataloader(envname, max_trajs=None, normalize=False, batch_size=32):
+    demos = np.load(
+        "../experts/{0}/demos.npz".format(envname), allow_pickle=True)
+    num_trajs = demos["num_trajs"]
+    if max_trajs is None:
+        max_trajs = num_trajs
+    obs = []
+    acts = []
+    for traj in range(min(max_trajs, num_trajs)):
+        obs.extend(demos[str(traj)].item()['states'])
+        acts.extend(demos[str(traj)].item()['actions'])
+    dataset = SADataset(obs, acts, normalize)
+    dataloader = DataLoader(dataset, batch_size=batch_size,
+                             shuffle=True, num_workers=0)
+    return dataloader
+
+class SADSDataset(torch.utils.data.Dataset):
+    def __init__(self, obs, acts, next_obs, traj_lens):
+        self.obs = torch.tensor(obs)
+        self.acts = torch.tensor(acts)
+        self.next_obs = torch.tensor(next_obs)
+        dones = [[False for _ in range(l - 2)] + [True] for l in traj_lens]
+        self.dones = torch.tensor(list(chain.from_iterable(dones)))
+
+    def __len__(self):
+        return len(self.obs)
+
+    def __getitem__(self, idx):
+        if torch.is_tensor(idx):
+            idx = idx.tolist()
+        obs = self.obs[idx]
+        acts = self.acts[idx]
+        next_obs = self.next_obs[idx]
+        dones = self.dones[idx]
+        sample = {'obs': obs, 'acts': acts,
+                  'next_obs': next_obs, 'dones': dones}
+        return sample
+
+def make_sads_dataloader(envname, max_trajs=None):
+    demos = np.load(
+        "./experts/{0}/demos.npz".format(envname), allow_pickle=True)
+    num_trajs = demos["num_trajs"]
+    if max_trajs is None:
+        max_trajs = num_trajs
+    obs = []
+    next_obs = []
+    acts = []
+    lens = []
+    for traj in range(min(max_trajs, num_trajs)):
+        obs.extend(demos[str(traj)].item()['states'][:-1])
+        next_obs.extend(demos[str(traj)].item()['states'][1:])
+        acts.extend(demos[str(traj)].item()['actions'][:-1])
+        lens.append(len(demos[str(traj)].item()['states']))
+    dataset = SADSDataset(obs, acts, next_obs, lens)
+    dataloader = DataLoader(dataset, batch_size=32,
+                            shuffle=False, num_workers=0, drop_last=True)
+    return dataloader
+
+def make_sa_dataset(envname, max_trajs=None):
+    demos = np.load("../pillbox/experts/{0}/demos.npz".format(envname), allow_pickle=True)
+    num_trajs = demos["num_trajs"]
+    if max_trajs is None:
+        max_trajs = num_trajs
+    expert_states = []
+    expert_actions = []
+    expert_next_states = []
+    expert_dones = []
+    for traj in range(min(max_trajs, num_trajs)):
+        expert_states.extend(demos[str(traj)].item()['states'][:-1])
+        expert_next_states.extend(demos[str(traj)].item()['states'][1:])
+        expert_actions.extend(demos[str(traj)].item()['actions'][:-1])
+        l = len(demos[str(traj)].item()['states'])
+        expert_dones.extend([False for _ in range(l - 2)] + [True])
+    expert_data = dict()
+    expert_data['obs'] = np.array(expert_states)
+    expert_data['acts'] = np.array(expert_actions)
+    expert_data['next_obs'] = np.array(expert_next_states)
+    expert_data['dones'] = np.array(expert_dones)
+    return expert_data

scratch/etienne/pillbox/requirements.txt

@@ -0,0 +1,9 @@
+gym
+numpy
+psutil
+scikit_learn
+scipy
+stable_baselines3
+torch
+tqdm
+imitation

scratch/johannes/evaluation.py

@@ -0,0 +1,90 @@
+
+def evaluate_policy_simple(
+    model,
+    env: gym.Env,
+    n_eval_episodes: int = 10,
+    deterministic: bool = True,
+    render: bool = False,
+    callback = None,
+    reward_threshold = None,
+    return_episode_rewards: bool = False,
+    warn: bool = True,
+):
+    """
+    Runs policy for ``n_eval_episodes`` episodes and returns average reward.
+    If a vector env is passed in, this divides the episodes to evaluate onto the
+    different elements of the vector env. This static division of work is done to
+    remove bias. See https://github.com/DLR-RM/stable-baselines3/issues/402 for more
+    details and discussion.
+
+    .. note::
+        If environment has not been wrapped with ``Monitor`` wrapper, reward and
+        episode lengths are counted as it appears with ``env.step`` calls. If
+        the environment contains wrappers that modify rewards or episode lengths
+        (e.g. reward scaling, early episode reset), these will affect the evaluation
+        results as well. You can avoid this by wrapping environment with ``Monitor``
+        wrapper before anything else.
+
+    :param model: The RL agent you want to evaluate.
+    :param env: The gym environment or ``VecEnv`` environment.
+    :param n_eval_episodes: Number of episode to evaluate the agent
+    :param deterministic: Whether to use deterministic or stochastic actions
+    :param render: Whether to render the environment or not
+    :param callback: callback function to do additional checks,
+        called after each step. Gets locals() and globals() passed as parameters.
+    :param reward_threshold: Minimum expected reward per episode,
+        this will raise an error if the performance is not met
+    :param return_episode_rewards: If True, a list of rewards and episode lengths
+        per episode will be returned instead of the mean.
+    :param warn: If True (default), warns user about lack of a Monitor wrapper in the
+        evaluation environment.
+    :return: Mean reward per episode, std of reward per episode.
+        Returns ([float], [int]) when ``return_episode_rewards`` is True, first
+        list containing per-episode rewards and second containing per-episode lengths
+        (in number of steps).
+    """
+    episode_rewards = []
+    episode_lengths = []
+
+    episode_counts = 0
+
+    current_rewards = 0
+    current_lengths = 0
+    observations = env.reset()
+    states = None
+    while (episode_counts < n_eval_episodes):
+        actions, states = model.predict(observations, state=states, deterministic=deterministic)
+        observations, rewards, dones, infos = env.step(actions)
+        print(env._env.t)
+        current_rewards += rewards
+        current_lengths += 1
+
+        # unpack values so that the callback can access the local variables
+        reward = rewards
+        done = dones
+        info = infos
+        if info['collision']:
+            print("COLLISION")
+
+        if callback is not None:
+            callback(locals(), globals())
+
+        if dones:
+            episode_rewards.append(current_rewards)
+            episode_lengths.append(current_lengths)
+            episode_counts += 1
+            current_rewards = 0
+            current_lengths = 0
+            if states is not None:
+                states *= 0
+
+        if render:
+            env.render()
+
+    mean_reward = np.mean(episode_rewards)
+    std_reward = np.std(episode_rewards)
+    if reward_threshold is not None:
+        assert mean_reward > reward_threshold, "Mean reward below threshold: " f"{mean_reward:.2f} < {reward_threshold:.2f}"
+    if return_episode_rewards:
+        return episode_rewards, episode_lengths
+    return mean_reward, std_reward

scratch/johannes/intersimple/gail_options_image.py

@@ -0,0 +1,101 @@
+# %%
+import sys
+sys.path.append('../../../')
+
+from src.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
+from imitation.algorithms import adversarial
+import stable_baselines3
+import torch.utils.data
+import numpy as np
+from intersim.envs.intersimple import NRasterized, speed_reward
+import itertools
+import functools
+from torch.distributions import Categorical
+import gym
+import torch
+import pickle
+import imitation.data.rollout as rollout
+import tempfile
+import pathlib
+from imitation.util import logger
+from stable_baselines3.common.env_util import make_vec_env
+from tqdm import tqdm
+from src.policies.options import OptionsCnnPolicy
+from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
+from src.gail.train import train_discriminator, train_generator
+from src.evaluation.evaluation import Evaluation
+from torch.utils.tensorboard import SummaryWriter
+
+
+model_name = 'gail_options_image'
+env_settings = {'agent': 51, 'width': 36, 'height': 36, 'm_per_px': 2}
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10]] # option 0 is safe fallback
+
+def train(expert_data, epochs=20, expert_batch_size=32, generator_steps=1024, discount=0.99):
+    env = NRasterized(**env_settings)
+    env.discount = discount
+
+    tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+    tempdir_path = pathlib.Path(tempdir.name)
+    logger.configure(tempdir_path / "GAIL/")
+    print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+    venv = make_vec_env(NRasterized, n_envs=1, env_kwargs=env_settings)
+    discriminator = adversarial.GAIL(
+        expert_data=expert_data,
+        expert_batch_size=expert_batch_size,
+        discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+        #discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        venv=venv, # unused
+        gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+    )
+
+    generator = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env, options=ALL_OPTIONS),
+        verbose=1,
+        n_steps=generator_steps,
+    )
+
+    # PPO.train requires logger as set up in
+    # PPO._setup_learn (called by PPO.learn)
+    generator._logger = stable_baselines3.common.utils.configure_logger(
+        generator.verbose,
+        generator.tensorboard_log,
+    )
+
+    filestr = os.path.join('out', model_name)
+    writer = SummaryWriter(filestr)
+    ev = Evaluation(filestr, env, expert_data, n_eval_episodes=100)
+    for epoch in tqdm(range(epochs)):
+        train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size)
+        train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
+
+        metrics = ev.evaluate(epoch, generator, discriminator)
+        for metric, value in metrics.items():
+            writer.add_scalar(metric, value, epoch)
+    
+    return generator
+
+# %%
+if __name__ == '__main__':
+    # %%
+ 
+    with open("scratch/etienne/intersimple/data/NormalizedIntersimpleExpertMu.001_NRasterizedInfoAgent51w36h36mppx2.pkl", "rb") as f:
+        trajectories = pickle.load(f)
+    transitions = rollout.flatten_trajectories(trajectories)
+    generator = train(transitions)
+
+    generator.save(model_name)
+
+    # %%
+    model = stable_baselines3.PPO.load(model_name)
+
+    env = RenderOptions(NRasterized(**env_settings), options=ALL_OPTIONS)
+
+    for s in env.sample_ll(model):
+        if s['dones']:
+            break
+
+    env.close(filestr='render/'+model_name)

scratch/johannes/intersimple/gail_options_image_random.py

@@ -0,0 +1,101 @@
+# %%
+import sys
+sys.path.append('../../../')
+
+from src.discriminator import CnnDiscriminatorFlatAction
+from imitation.algorithms import adversarial
+import stable_baselines3
+import torch.utils.data
+import numpy as np
+from intersim.envs.intersimple import NRasterizedRandomAgent
+import itertools
+from torch.distributions import Categorical
+import gym
+import torch
+import pickle
+import imitation.data.rollout as rollout
+import tempfile
+import pathlib
+from imitation.util import logger
+from stable_baselines3.common.env_util import make_vec_env
+from tqdm import tqdm
+from src.policies.options import OptionsCnnPolicy
+from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
+from src.gail.train import train_discriminator, train_generator
+from src.evaluation.evaluation import Evaluation
+from torch.utils.tensorboard import SummaryWriter
+import os 
+
+model_name = 'gail_options_image_random'
+env_settings = {'width': 36, 'height': 36, 'm_per_px': 2}
+
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is safe fallback
+
+def train(expert_data, epochs=100, expert_batch_size=16, generator_steps=16, discount=0.99):
+    env = NRasterizedRandomAgent(**env_settings)
+    env.discount = discount
+
+    tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+    tempdir_path = pathlib.Path(tempdir.name)
+    logger.configure(tempdir_path / "GAIL/")
+    print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+    venv = make_vec_env(NRasterizedRandomAgent, n_envs=1, env_kwargs=env_settings)
+    discriminator = adversarial.GAIL(
+        expert_data=expert_data,
+        expert_batch_size=expert_batch_size,
+        discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+        #discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        venv=venv, # unused
+        gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
+    )
+
+    generator = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env, options=ALL_OPTIONS),
+        verbose=1,
+        n_steps=generator_steps,
+    )
+
+    # PPO.train requires logger as set up in
+    # PPO._setup_learn (called by PPO.learn)
+    generator._logger = stable_baselines3.common.utils.configure_logger(
+        generator.verbose,
+        generator.tensorboard_log,
+    )
+    
+    filestr = os.path.join('out', model_name)
+    writer = SummaryWriter(filestr)
+    ev = Evaluation(filestr, env, expert_data, n_eval_episodes=100)
+    for epoch in tqdm(range(epochs)):
+        train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size)
+        train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
+        generator.save(model_name)
+    
+        metrics = ev.evaluate(epoch, generator, discriminator)
+        for metric, value in metrics.items():
+            writer.add_scalar(metric, value, epoch)
+    
+    return generator
+
+def video(model_name, env):
+    model = stable_baselines3.PPO.load(model_name)
+    env = RenderOptions(env, options=ALL_OPTIONS)
+    for s in env.sample_ll(model):
+        if s['dones']:
+            break
+    env.close(filestr='render/'+model_name)
+
+def evaluate():
+    video(
+        model_name=model_name,
+        env=NRasterizedRandomAgent(**env_settings)
+    )
+
+# %%
+if __name__ == '__main__':
+
+    with open("scratch/etienne/intersimple/data/NormalizedIntersimpleExpertMu.001N10000_NRasterizedRandomAgentInfow36h36mppx2.pkl", "rb") as f:
+        trajectories = pickle.load(f)
+    transitions = rollout.flatten_trajectories(trajectories)
+    train(transitions)

scratch/johannes/normalization.py

@@ -0,0 +1,73 @@
+import torch
+from torch import nn
+
+from sklearn import preprocessing
+
+class Normalization(nn.Module):
+    def __init__(self, X):
+        super(Normalization, self).__init__()
+        self.fit(X)
+
+    def fit(self, X):
+        raise NotImplementedError('Please implement fit()')
+
+    def transform(self, X):
+        raise NotImplementedError('Please implement transform()')
+
+    def inverse_transform(self, X):
+        raise NotImplementedError('Please implement inverse_transform()')
+
+    def forward(self, X):
+        return self.transform(X)
+
+class SciKitNormalization(Normalization):
+    def __init__(self, tf, X):
+        self.tf = tf
+        super(SciKitNormalization, self).__init__(X)
+
+    def fit(self, X):
+        self.tf.fit(X)
+
+    def transform(self, X):
+        return torch.tensor(self.tf.transform(X), dtype=torch.float)
+
+    def inverse_transform(self, X):
+        return torch.tensor(self.tf.inverse_transform(X), dtype=torch.float)
+
+class SciKitStandardization(SciKitNormalization):
+    def __init__(self, X):
+        super(SciKitStandardization, self).__init__(preprocessing.StandardScaler(), X)
+
+class SciKitMinMaxScaler(SciKitNormalization):
+    def __init__(self, X):
+        super(SciKitMinMaxScaler, self).__init__(preprocessing.MinMaxScaler(), X)
+
+
+ns = 5
+na = 1
+n_batch = 1000
+
+state = torch.rand(n_batch, ns)
+action = torch.rand(n_batch, na)
+
+s_tf = SciKitStandardization(state)
+a_tf = SciKitMinMaxScaler(action)
+
+print(torch.linalg.norm(s_tf.inverse_transform(s_tf(state)) - state))
+print(torch.linalg.norm(a_tf.inverse_transform(a_tf(action)) - action))
+
+
+
+
+# class Foo:
+#     def __init__(self):
+#         return None
+#     def baz(self):
+#         print("Foo.baz()")
+
+# class Bar(Foo):
+#     def __init__(self):
+#         return None
+
+# bar = Bar()
+# bar.baz()

scratch/johannes/raytune_simple.py

@@ -0,0 +1,69 @@
+"""This example demonstrates basic Ray Tune random search and grid search."""
+import time
+
+import ray
+from ray import tune
+
+
+def evaluation_fn(step, width, height):
+    time.sleep(0.1)
+    return (0.1 + width * step / 100)**(-1) + height * 0.1
+
+def easy_objective(config):
+    # Hyperparameters
+    width, height = config["width"], config["height"]
+
+    mydata = ray.get(ray_data)
+    print(mydata)
+
+    for step in range(config["steps"]):
+        # Iterative training function - can be any arbitrary training procedure
+        intermediate_score = evaluation_fn(step, width, height)
+        # Feed the score back back to Tune.
+        tune.report(iterations=step, mean_loss=intermediate_score)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Finish quickly for testing")
+    parser.add_argument(
+        "--server-address",
+        type=str,
+        default=None,
+        required=False,
+        help="The address of server to connect to if using "
+        "Ray Client.")
+    args, _ = parser.parse_known_args()
+    if args.server_address is not None:
+        ray.init(f"ray://{args.server_address}")
+    else:
+        ray.init(configure_logging=False)
+
+    # This will do a grid search over the `activation` parameter. This means
+    # that each of the two values (`relu` and `tanh`) will be sampled once
+    # for each sample (`num_samples`). We end up with 2 * 50 = 100 samples.
+    # The `width` and `height` parameters are sampled randomly.
+    # `steps` is a constant parameter.
+
+    import numpy as np
+    N = 3
+    data = np.random.rand(N,N,N)
+    ray_data = ray.put(data)
+
+
+    analysis = tune.run(
+        easy_objective,
+        metric="mean_loss",
+        mode="min",
+        num_samples=5 if args.smoke_test else 50,
+        config={
+            "steps": 5 if args.smoke_test else 100,
+            "width": tune.uniform(0, 20),
+            "height": tune.uniform(-100, 100),
+            "activation": tune.grid_search(["relu", "tanh"])
+        })
+
+    print("Best hyperparameters found were: ", analysis.best_config)

src/__init__.py

@@ -1 +1,3 @@
-from src.expert_data import generate_expert_data, load_expert_data
+from src.data.expert_data import generate_expert_data, load_expert_data
+from src.data.data_utils import InteractionDatasetSingleAgent
+from src.evaluation.metrics import metrics

src/bc/__init__.py

@@ -0,0 +1 @@
+from src.bc.bc import BehaviorCloningPolicy, train, bc_config

src/bc/bc.py

@@ -0,0 +1,191 @@
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import pickle
+from torch.utils.tensorboard import SummaryWriter
+
+from src.policies import IntersimStateNet, IntersimPolicy, generate_transforms
+from src.util.nn_training import optimizer_factory
+from tqdm import tqdm
+import json5
+from ray import tune
+
+def bc_config(ray_config):
+    config = {
+        'ego_encoder': {'input_dim': 5, 'hidden_n': 0, 'hidden_dim':0, 'output_dim': 0},
+        'deepsets': {
+            'input_dim': 6,
+            'phi': {
+                'hidden_n': ray_config['deepsets_phi_hidden_n'],
+                'hidden_dim': ray_config['deepsets_phi_hidden_dim']
+                },
+            'latent_dim': ray_config['deepsets_latent_dim'],
+            'rho': {
+                'hidden_n': ray_config['deepsets_rho_hidden_n'],
+                'hidden_dim': ray_config['deepsets_rho_hidden_dim']
+                },
+            'output_dim': ray_config['deepsets_output_dim']
+        },
+        'path_encoder': {'input_dim': 40, 'hidden_n': 0, 'hidden_dim': 0, 'output_dim': 0},
+        'head': {
+            'input_dim': 0, # computed in constructor
+            'hidden_n': ray_config['head_hidden_n'],
+            'hidden_dim': ray_config['head_hidden_dim'],
+            'output_dim': 1, # number of outputs e.g. number of actions, or just one
+            'final_activation': ray_config['head_final_activation'],
+        },
+        'optim': {
+            'optimizer':'adam',
+            'lr':ray_config['lr'],
+            'weight_decay':ray_config['weight_decay']
+        },
+        'train_epochs': 40,
+        'train_batch_size': ray_config['train_batch_size'],
+        'loss': ray_config['loss'],
+
+    }
+    return config
+
+class BehaviorCloningPolicy(IntersimPolicy):
+    """
+    Class for (continuous) behavior cloning policy
+    """
+    
+    def __init__(self, config: dict, transforms: dict):
+        """
+        Initialize BehaviorCloningPolicy
+        Args:
+            config (dict): configuration file to initialize IntersimDeepSetsNet with
+            transforms (dict): dictionary of transforms to apply to different fields
+        """
+        super(BehaviorCloningPolicy, self).__init__(config, transforms)
+        self._policy = IntersimStateNet(config)
+        
+    @classmethod
+    def load_model(cls, filestr: str, config: dict = None):
+        """
+        Load a model from a file prefix
+        Args:
+            config (dict): configuration dict to set up model
+            filestr (str): string prefix to load model from
+        Returns
+            model (BehaviorCloningPolicy): loaded model
+        """
+        if not config:
+            with open(filestr+'_config.json', 'r') as cfg:
+                config = json5.load(cfg)
+        transforms = pickle.load(open(filestr+'_transforms.pkl', 'rb'))
+        model = cls(config, transforms=transforms)
+        model._policy.load_state_dict(torch.load(filestr+'_model.pt'))
+        return model
+    
+    def eval(self):
+        self._policy.eval()
+
+    def parameters(self):
+        return self._policy.parameters()
+
+    def save_model(self, filestr, save_config=True, save_transforms=True):
+        """
+        Save transforms and state_dict to a location specificed by filestr
+        Args:
+            filestr (str): string prefix to save model to
+            save_config (bool): whether to save the config file (as a json)
+            save_transforms (bool): whether to save transforms (as a pickle)
+        """
+        if save_config:
+            with open(filestr+'_config.json', 'w') as cfg:
+                json5.dump(self._config, cfg)
+        if save_transforms:
+            pickle.dump(self._transforms, open(filestr+'_transforms.pkl', 'wb'))
+        torch.save(self._policy.state_dict(), filestr+'_model.pt')
+
+def train(config, policy, train_dataset, cv_dataset, filestr, **kwargs):
+    
+    using_ray = kwargs.get('ray', False)
+    if using_ray:
+        print('using ray')
+
+    # hyperparams
+    loss_type = config['loss']
+    train_epochs = config['train_epochs']
+    train_batch_size = config['train_batch_size']
+
+    cv_every = 1
+    print_epoch_every = 1000
+    print_cv_every = 5
+    checkpoint_every = 100
+    cv_batch_size = 256 # doesn't matter
+
+    # training and testing dataloaders
+    training_loader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)
+    cv_loader = DataLoader(cv_dataset, batch_size=cv_batch_size, shuffle=True)
+
+    # change policy dtype
+    policy.policy = policy.policy.type(train_dataset[0]['state']['ego_state'].dtype)
+
+    # generate loss function, optimizer
+    cv_loss_fn = nn.MSELoss(reduction='sum')
+    if loss_type == 'huber':
+        loss_fn = nn.HuberLoss(reduction='sum') 
+    elif loss_type == 'mse':
+        loss_fn = nn.MSELoss(reduction='sum')
+    else:
+        raise NotImplementedError
+    optimizer = optimizer_factory(config['optim'], policy.parameters())
+
+    # generate tensorboard writer
+    if not using_ray:
+        writer = SummaryWriter(filestr)
+    
+    for i in tqdm(range(train_epochs)):
+        
+        # save model checkpoints
+        if i % checkpoint_every == 0:
+            policy.save_model(filestr + '_epoch%04i'%(i) )
+
+        # train
+        epoch_loss = 0
+        for (batch_idx, batch) in enumerate(training_loader):
+            
+            # sample mini-batch and run through policy
+            pred_action = policy(batch['state']) 
+            loss = loss_fn(pred_action, batch['action'])
+
+            # compute loss and step optimizer
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            epoch_loss += loss.item() / len(train_dataset)
+        
+        # if i % print_epoch_every == 0:
+        #     print('Epoch: {}, Training Loss: {}'.format(i, epoch_loss))
+
+                # measure cv loss
+
+        if i % cv_every == 0:
+            with torch.no_grad():
+                cv_loss = 0.
+                for (batch_idx, batch) in enumerate(cv_loader):
+                    pred_action = policy(batch['state']) 
+                    loss = cv_loss_fn(pred_action, batch['action'])
+                    cv_loss += loss.item() / len(cv_dataset)
+            
+            
+        # Write epoch loss
+        if using_ray:
+            if i % cv_every == 0:
+                tune.report(training_loss=epoch_loss, cv_loss=cv_loss, training_iteration=i+1)
+            else:
+                tune.report(training_loss=epoch_loss, training_iteration=i+1)
+        else:
+            writer.add_scalar('training loss',epoch_loss, i)
+            if i % cv_every == 0:
+                writer.add_scalar('cv loss', cv_loss, i)
+
+        # if i % print_cv_every == 0:    
+        #     print('Epoch: {}, CV Loss: {}'.format(i, cv_loss))
+
+
+    policy.save_model(filestr)

src/data/__init__.py

@@ -0,0 +1 @@
+from src.data.expert import single_agent_expert, single_agent_demonstrations, multi_agent_demonstrations, load_experts, process_experts 

src/data/data_utils.py

@@ -0,0 +1,96 @@
+import torch
+from torch.utils.data import Dataset
+import numpy as np
+from src.data.expert_data import load_expert_data
+import os
+opj = os.path.join
+
+class InteractionDatasetMultiAgent(Dataset):
+    """
+    Class to handle getting full multi-agent observations and actions
+    """
+    pass
+
+class InteractionDatasetSingleAgent(Dataset):
+    """Class to load states and actions for individual agents."""
+
+    def __init__(self, output_dir='expert_data', loc:int = 0, tracks:list = [0], dtype=torch.float32):
+        """
+        Args:
+            output_dir (string): Directory with all the images.
+            loc (int): location index
+            tracks (list[int]): track indices
+        """
+        self.output_dir = output_dir
+        self.loc = loc
+        self.tracks = tracks
+        self.dtype = dtype
+        self.keys = ['ego_state', 'relative_state', 'path', 'action', 'next_ego_state', 'next_relative_state', 'next_path']
+        self._load_dataset()
+
+    def _load_dataset(self):
+        """
+        Load the full datasets ahead of time
+        """
+        self.raw_data = {key:[] for key in self.keys}
+        max_nv = 0
+        for track in self.tracks:
+            try:
+                data = load_expert_data(path=self.output_dir, loc=self.loc, track=track)
+                print('Loaded location {} track {}'.format(self.loc,track))
+            except:
+                print('Failed to load location {} track {}'.format(self.loc,track))
+                continue
+            max_nv = max(max_nv, data['relative_state'].shape[1])
+            for key in self.keys:
+                self.raw_data[key].append(data[key])
+
+        # pad second dimension of relative state
+        for i in range(len(self.raw_data['relative_state'])):
+            nv1, nv2, d = self.raw_data['relative_state'][i].shape
+            pad = torch.zeros(nv1, max_nv-nv2, d, dtype=self.dtype) * np.nan
+            self.raw_data['relative_state'][i] = torch.cat((self.raw_data['relative_state'][i], pad), dim=1)
+            self.raw_data['next_relative_state'][i] = torch.cat((self.raw_data['next_relative_state'][i], pad), dim=1)
+
+        # cat lists
+        for key in self.keys:
+            self.raw_data[key] = torch.cat(self.raw_data[key]).type(self.dtype)
+
+        # mandate equal length
+        lengths = [len(self.raw_data[key]) for key in self.keys]
+        assert min(lengths) == max(lengths), 'dataset lengths unequal'
+        
+    def __len__(self):
+        return len(self.raw_data['ego_state'])
+
+    def __getitem__(self, idx):
+        """ 
+        Sample from the dataset
+        Args:
+            idx: index or indices of B samples
+        Returns:
+            sample (dict): sample dictionary with the following entries:
+                state (dict): state dictionary with the following entries:
+                    ego_state (torch.tensor): (B, 5) raw state
+                    relative_state (torch.tensor): (B, max_nv, d) relative state (padded with nans)
+                    path (torch.tensor): (B, P, 2) tensor of P future path x and y positions
+                action (torch.tensor): (B, 1) actions taken from each state
+                next_stat (dict): next state dictionary with the following entries:
+                    ego_state (torch.tensor): (B, 5) raw next state
+                    relative_state (torch.tensor): (B, max_nv, d) next relative state (padded with nans)
+                    path (torch.tensor): (B, P, 2) tensor of P future next path x and y positions
+        """
+        #sample = {key:self.raw_data[key][idx] for key in self.keys}
+        sample = {
+            'state':{
+                'ego_state':self.raw_data['ego_state'][idx],
+                'relative_state':self.raw_data['relative_state'][idx],
+                'path':self.raw_data['path'][idx]
+            },
+            'action':self.raw_data['action'][idx],
+            'next_state':{
+                'ego_state':self.raw_data['next_ego_state'][idx],
+                'relative_state':self.raw_data['next_relative_state'][idx],
+                'path':self.raw_data['next_path'][idx]},
+        }
+        return sample

src/data/expert.py

@@ -0,0 +1,241 @@
+import intersim
+from intersim.envs.intersimple import Intersimple
+from stable_baselines3.common.policies import BasePolicy
+import gym
+import intersim.envs.intersimple
+import pickle
+from tqdm import tqdm
+import copy
+import os
+import numpy as np
+
+from src.util.rollout import rollout_and_save, flatten_trajectories, make_sample_until
+
+class IntersimExpert(BasePolicy):
+    
+    def __init__(self, intersim_env, mu=0, *args, **kwargs):
+        super().__init__(
+            observation_space=gym.spaces.Space(),
+            action_space=gym.spaces.Space(),
+            *args, **kwargs
+        )
+        self._intersim = intersim_env
+        self._mu = mu
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _action(self):
+        target_t = min(self._intersim._ind + 1, len(self._intersim._svt.simstate) - 1)
+        target_state = self._intersim._svt.simstate[target_t]
+        return self._intersim.target_state(target_state, mu=self._mu)
+
+    def predict(self, *args, **kwargs):
+        return self._action(), None
+
+class IntersimpleExpert(BasePolicy):
+
+    def __init__(self, intersimple_env, mu=0, *args, **kwargs):
+        super().__init__(
+            observation_space=intersimple_env.observation_space,
+            action_space=intersimple_env.action_space,
+            *args, **kwargs
+        )
+        self._intersimple = intersimple_env
+        self._intersim_expert = IntersimExpert(intersimple_env._env, mu=mu)
+    
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _predict(self, *args, **kwargs):
+        raise NotImplementedError()
+
+    def _action(self):
+        return self._intersim_expert._action()[self._intersimple._agent]
+
+    def predict(self, *args, **kwargs):
+        return self._action(), None
+
+class NormalizedIntersimpleExpert(IntersimpleExpert):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def predict(self, *args, **kwargs):
+        action, _ = super().predict(*args, **kwargs)
+        return self._intersimple._normalize(action), None
+
+def save_video(env, expert):
+    env.reset()
+    env.render()
+    done = False
+    while not done:
+        actions, _ = expert.predict()
+        _, _, done, _ = env.step(actions)
+        env.render()
+    env.close()
+
+def load_experts(expert_files, flatten=True):
+    """
+    Load expert trajectories from files and combine their transitions into a single RB
+
+    Args:
+        expert_files (list): list of expert file strings
+        flatten (bool): whether to flatten trajectory info
+    Returns:
+        transitions (list): list of combined expert episode transitions 
+    """
+    transitions = []
+    for file in tqdm(expert_files):
+        with open(file, "rb") as f:
+            new_trajectories = pickle.load(f)
+        transitions += new_trajectories
+    if flatten:
+        transitions = flatten_trajectories(transitions)
+    return transitions
+
+def single_agent_expert(expert='NormalizedIntersimpleExpert', 
+                        env='NRasterizedRouteIncrementingAgent', 
+                        env_args={}, policy_args={}, **kwargs):
+    """
+    Args: 
+        expert (class): class of expert
+        env (class): class of env intersim.envs.intersimple
+        env_args (dict): dictionary of kwargs when instantiating environment class
+        policy_args (dict): dictionary of kwargs when instantiating Expert policy
+        path (str): path to store output
+        min_timesteps (int): min number of timesteps for call to rollout.rollout_and_save
+        min_episodes (int): min number of episodes for call to rollout.rollout_and_save
+        video (bool): whether to save a video of the expert until a single environment instantiation stops
+    """
+    Env = intersim.envs.intersimple.__dict__[env]
+    Expert = globals()[expert]
+    env = Env(**env_args)
+    policy = Expert(env, **policy_args)
+    single_agent_demonstrations(env, policy, **kwargs)
+
+def single_agent_demonstrations(env, policy, 
+                                path=None, min_timesteps=None, 
+                                min_episodes=None, video=False,
+                                env_args={}, policy_args={}):
+    """Rollout and save expert demos.
+    
+    Usage:
+        python -m intersimple.expert <flags>
+    Args: 
+        env (class): intersimple environment
+        policy (BasePolicy): intersimple policy
+        path (str): path to store output
+        min_timesteps (int): min number of timesteps for call to rollout.rollout_and_save
+        min_episodes (int): min number of episodes for call to rollout.rollout_and_save
+        video (bool): whether to save a video of the expert until a single environment instantiation stops
+        env_args (dict): dictionary of kwargs when instantiating environment class
+        policy_args (dict): dictionary of kwargs when instantiating Expert policy
+    """
+    
+    if min_timesteps is None and min_episodes is None:
+        min_episodes = env.nv # one episode per vehicle being controlled in environment (hopefully an incrementing agent environment)
+
+    if video:
+        save_video(env, policy)
+
+    path = path or (policy.__class__.__name__ + '_' + env.__class__.__name__ + '.pkl')
+    suntil = make_sample_until(min_timesteps=min_timesteps,min_episodes=min_episodes)
+    
+    rollout_and_save(
+        path=path,
+        policy=policy,
+        env=env,
+        sample_until=suntil
+    )
+
+def multi_agent_demonstrations(expert='IntersimExpert',path=None, env_args={}, policy_args={}):
+    """
+    Run and save the `intersim' multiagent environment demonstration
+    
+    Args: 
+        expert (class): class of multi-agent expert
+        path (str): path to store output data
+        env_args (dict): dictionary of kwargs when instantiating environment class
+        policy_args (dict): dictionary of kwargs when instantiating Expert policy
+    """
+    if path is None:
+        raise('No path specified')
+
+    env = gym.make('intersim:intersim-v0',**env_args)
+    Expert = globals()[expert] 
+    policy = Expert(env, **policy_args)
+
+    s, done = env.reset(), False
+    env.render(mode='file')
+    while not done:
+        _,_,done,_ = env.step(policy.predict()[0])
+        env.render(mode='file')
+    env.close(filestr=path)
+
+def process_experts(filename:str='expert.pkl', 
+                    locs:list=None, 
+                    tracks:list=None,
+                    env_class:str='NRasterizedRouteIncrementingAgent',
+                    env_args:dict={'width':36,'height':36,'m_per_px':2},
+                    expert_class:str='NormalizedIntersimpleExpert',  
+                    expert_args:dict={'mu':0.001}):
+    """
+    Process all experts in the Interaction Dataset
+    For now, using NormalizedIntersimpleExpert with NRasterizedRouteIncrementingAgent environment
+
+    Args:
+        filename (str): name for track file
+        locs (list): list of location ids
+        tracks (list): list of track numbers
+        env_class (str): class of environment
+        env_args (dict): default environment kwargs
+        expert_class (str): class of expert
+        expert_args (dict): default expert kwargs
+    """
+    locs = locs or intersim.LOCATIONS
+    tracks = tracks or range(intersim.MAX_TRACKS)
+    pbar = tqdm(total=len(locs)*len(tracks))
+    for loc in locs:
+        for track in tracks:
+            
+            iloc = intersim.LOCATIONS.index(loc)
+
+            it_env_args = copy.deepcopy(env_args)
+            env_loc_args = {
+                'loc':iloc,
+                'track':track,
+            }
+            it_env_args.update(env_loc_args)
+            out_folder = os.path.join('expert_data',loc, 'track%04i'%(track))
+            if not os.path.isdir(out_folder):
+                os.makedirs(out_folder) 
+            it_path = os.path.join(out_folder,filename)
+
+            # Multi-Agent demonstrations
+            it_ma_path = os.path.join(out_folder,'joint_expert')
+            multi_agent_demonstrations(
+                expert='IntersimExpert',
+                path=it_ma_path,
+                env_args=env_loc_args,
+                policy_args=expert_args
+            )
+            # Single-Agent POV Demonstrations
+            single_agent_expert(
+                expert=expert_class, 
+                env=env_class, 
+                path=it_path, 
+                env_args=it_env_args, 
+                policy_args=expert_args,
+                )
+            
+            pbar.update(1)
+    pbar.close()
+    
+if __name__=='__main__':
+    import fire
+    fire.Fire(process_experts)
+

src/data/expert_data.py

@@ -0,0 +1,210 @@
+import torch
+
+import pickle
+import gym
+import numpy as np
+
+import intersim
+from intersim.utils import get_map_path, get_svt, SVT_to_stateactions
+from intersim import collisions
+from intersim.graphs import ConeVisibilityGraph
+import os
+opj = os.path.join
+
+def generate_expert_data(path: str='expert_data', loc: int = 0, track:int = 0, 
+                            mask_relstate: bool = False, regularize_actions: bool = False, 
+                            **kwargs):
+    """
+    Function to save (joint) states and observations from simulated frame
+    Args:
+        path (str): directory to save data
+        loc (int): location index
+        track (int): track index
+        mask_relstate (bool): whether to mask the relative states from the cone visibility graph
+        regularize_actions (bool): whether to regularize the action selection
+        kwargs: arguments for environment instantiation
+    """
+    
+    action_reg = 0.002 if regularize_actions else 0
+    
+    if not os.path.isdir(path):
+        os.makedirs(path)
+    filestr = opj(path,intersim.LOCATIONS[loc]+'_track%03i'%(track))
+    
+    svt, svt_path = get_svt(loc=loc, track=track) #base='InteractionSimulator'
+    osm = get_map_path(loc=loc)
+    print('SVT path: {}'.format(svt_path))
+    print('Map path: {}'.format(osm))
+    states, actions = SVT_to_stateactions(svt)
+
+    # animate from environment
+    if mask_relstate:
+        cvg = ConeVisibilityGraph(r=20, half_angle=120)
+        env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, 
+            min_acc=-np.inf, max_acc=np.inf, graph=cvg, mask_relstate=True, **kwargs)
+    else:
+        env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, **kwargs, 
+            min_acc=-np.inf, max_acc=np.inf)
+    
+    env.reset()
+    done = False
+    obs, actions_taken, max_devs = [], [], []
+    i = 0
+    while not done and i < len(actions):
+        # check state deviation
+        env_state = env.projected_state
+        nni = ~torch.isnan(env_state[:,0])
+        norms = torch.norm(env_state[nni,:2]-states[i,nni,:2], dim=1)
+        if len(norms)>0:
+            max_devs.append(norms.max())
+
+        # propagate environment
+        ob, r, done, info = env.step(env.target_state(svt.simstate[i+1], mu=action_reg))
+        obs.append(ob)
+        actions_taken.append(info['action_taken'])
+        i += 1
+    
+    print("Maximum environment deviation from track: %f m" %(max(max_devs)))
+
+    # check for collisions
+    x = torch.stack([ob['state'] for ob in obs])
+    cols = collisions.check_collisions_trajectory(x, svt.lengths, svt.widths)
+    assert ~torch.any(cols), 'Error: Collisions found at indices {}'.format(cols.nonzero(as_tuple=True))
+
+    # shift actions
+    actions_taken.pop(0)
+    obs.pop(-1)
+    actions = torch.stack(actions_taken)
+
+    # save observations and actions
+    pickle.dump(obs,open(filestr+'_raw_observations.pkl', 'wb'))
+    torch.save(actions, filestr+'_raw_actions.pt')
+    process_expert_observations(obs, actions, filestr)
+
+def process_expert_observations(obs, actions, filestr, remove_outliers=True, dtype=torch.float32):
+    """
+    Process the expert observations and save them as torch tensors
+    Args:
+        obs (list[dict]): lost of observations
+        actions (torch.Tensor): (T, nv, a) tensor of actions
+        filestr (str): base filename with which to save out observation tensors
+        remove_outliers (bool): whether to remove datapoints with acceleration above or below 5 m/s/s
+        dtype (torch.Type): type to convert data to
+    """
+    keys = ['ego_state', 'relative_state', 'path', 'action', 'next_ego_state', 'next_relative_state', 'next_path']
+    data = {key:[] for key in keys}
+    assert len(obs) == len(actions), 'non-matching action and observation lengths'
+    T = len(obs)
+    max_nv = 0
+    for t in range(T-1):
+        nni = ~torch.isnan(obs[t]['state'][:,0]) & ~torch.isnan(obs[t+1]['state'][:,0]) 
+        max_nv = max(max_nv,nni.count_nonzero())
+
+        # state
+        data['ego_state'].append(obs[t]['state'][nni])
+        data['relative_state'].append(obs[t]['relative_state'].index_select(0, 
+            nni.nonzero()[:,0]).index_select(1, nni.nonzero()[:,0]))
+        data['path'].append(torch.stack((obs[t]['paths'][0][nni], obs[t]['paths'][1][nni]), dim=-1))
+
+        # action
+        data['action'].append(actions[t][nni])
+
+        # next state
+        data['next_ego_state'].append(obs[t+1]['state'][nni])
+        data['next_relative_state'].append(obs[t+1]['relative_state'].index_select(0, 
+            nni.nonzero()[:,0]).index_select(1, nni.nonzero()[:,0]))
+        data['next_path'].append(torch.stack((obs[t+1]['paths'][0][nni], obs[t+1]['paths'][1][nni]), dim=-1))
+
+    
+
+    # pad second dimension of relative state
+    for i in range(len(data['relative_state'])):
+        nv1, nv2, d = data['relative_state'][i].shape
+        pad = torch.zeros(nv1, max_nv-nv2, d, dtype=dtype) * np.nan
+        data['relative_state'][i] = torch.cat((data['relative_state'][i], pad), dim=1)
+        data['next_relative_state'][i] = torch.cat((data['next_relative_state'][i], pad), dim=1)
+
+    # cat lists 
+    for key in keys:
+        data[key] = torch.cat(data[key]).type(dtype)
+
+    if remove_outliers:
+        non_outlier_indices = torch.nonzero(torch.abs(data['action'][:,0]) < 5)
+        for key in keys:
+            data[key] = data[key][non_outlier_indices[:,0]]    
+
+    # mandate equal length
+    lengths = [len(data[key]) for key in keys]
+    assert min(lengths) == max(lengths), 'dataset lengths unequal'
+
+    # save out data
+    for key in keys:
+        torch.save(data[key], filestr+'_'+key+'.pt')
+
+def load_expert_data(path='expert_data', loc: int = 0, track:int = 0):
+    """
+    Load expert data from processed files.
+    Args:
+        path (str): directory to save data
+        loc (int): location index
+        track (int): track index
+    Returns:
+        data (dict[torch.Tensor]): dict of data
+    """
+    # load observations and actions
+    filestr = opj(path, intersim.LOCATIONS[loc]+'_track%03i'%(track))
+    data = {}
+    keys = ['ego_state', 'relative_state', 'path', 'action', 'next_ego_state', 'next_relative_state', 'next_path']
+    for key in keys:
+        data[key] = torch.load(filestr+'_'+key+'.pt')
+    return data
+
+def load_expert_data_raw(path='expert_data', loc: int = 0, track:int = 0):
+    """
+    Load expert data from raw file.
+    Args:
+        path (str): directory to save data
+        loc (int): location index
+        track (int): track index
+    Returns:
+        obs (list[Observations]): list of observations
+        actions (list[torch.tensor]): list of corresponding actions taken in observations
+    """
+    # load observations and actions
+    filestr = opj(path, intersim.LOCATIONS[loc]+'_track%03i'%(track))
+    obs = pickle.load(open(filestr+'_raw_observations.pkl', 'rb'))
+    actions = torch.load(filestr+'_raw_actions.pt')
+    actions = list(torch.unbind(actions))
+    return obs, actions
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser(description='Save Expert Trajectories')
+    parser.add_argument('--loc', default=0, type=int,
+                       help='location (default 0)')
+    parser.add_argument('--track', default=0, type=int,
+                       help='track number (default 0)')
+    parser.add_argument('--all-tracks', action='store_true',
+                       help='whether to process all tracks at location')
+    parser.add_argument('--graph', action='store_true',
+                       help='whether to mask the relative states based on a ConeVisibilityGraph')
+    parser.add_argument('--reg', action='store_true',
+                       help='whether to regularize actions in the action targeter')
+    parser.add_argument('-o', default='./expert_data', type=str,
+                       help='output folder')
+    args = parser.parse_args()
+
+    kwargs = {
+        'loc':args.loc, 
+        'track': args.track,
+        'path':args.o, 
+        'mask_relstate':args.graph, 
+        'regularize_actions': args.reg
+    }
+
+    if args.all_tracks:
+        for i in range(intersim.MAX_TRACKS):
+            kwargs['track'] = i
+            generate_expert_data(**kwargs)
+    else:
+        generate_expert_data(**kwargs)

src/data_utils.py

@@ -1,103 +0,0 @@
-import torch
-from torch.utils.data import Dataset, DataLoader
-import numpy as np
-#from torchvision import transforms, utils
-from src.expert_data import load_expert_data
-import os
-opj = os.path.join
-
-class InteractionDatasetMultiAgent(Dataset):
-    """
-    Class to handle getting full multi-agent observations and actions
-    """
-    pass
-
-class InteractionDatasetSingleAgent(Dataset):
-    """Class to load states and actions for individual agents."""
-
-    def __init__(self, output_dir='expert_data', loc:int = 0, tracks:list = [0], transforms={}):
-        """
-        Args:
-            output_dir (string): Directory with all the images.
-            loc (int): location index
-            tracks (list[int]): track indices
-            transforms (dict): dictionary of transforms to apply to different variables
-        """
-        self.output_dir = output_dir
-        self.loc = loc
-        self.tracks = tracks
-        self.transforms = transforms
-        #self.action_transform = transforms.get('action', None)
-        #self.state_transform = transforms.get('state', None)
-        #self.relative_state_transform = transforms.get('relative_state', None)
-        #self.paths_x_transform = transforms.get('paths_x', None)
-        #self.paths_y_transform = transform.get('paths_y',None)
-
-        self._load_dataset()
-
-    def _load_dataset(self):
-        """
-        Load the full datasets ahead of time
-        """
-        self.raw_data = {'state':[], 'relative_state':[], 'action':[], 'path_x':[], 'path_y':[]}
-        max_nv = 0
-        for track in self.tracks:
-            try:
-                observations, actions = load_expert_data(path=self.output_dir, loc=self.loc, track=track)
-                print('Loaded location {} track {}'.format(self.loc,track))
-            except:
-                print('Failed to load location {} track {}'.format(self.loc,track))
-                continue
-            T = len(actions)
-            for t in range(T):
-                nni = ~torch.isnan(observations[t]['state'][:,0])
-                max_nv = max(max_nv,nni.count_nonzero())
-                self.raw_data['state'].append(observations[t]['state'][nni])
-                self.raw_data['relative_state'].append(observations[t]['relative_state'][nni.nonzero(),nni.nonzero()])
-                self.raw_data['action'].append(actions[t][nni])
-                self.raw_data['path_x'].append(observations[t]['paths'][0][nni])
-                self.raw_data['path_y'].append(observations[t]['paths'][1][nni])
-
-        # cat lists
-        self.raw_data['state'] = torch.cat(self.raw_data['state'])
-        self.raw_data['action'] = torch.cat(self.raw_data['action'])
-        self.raw_data['path_x'] = torch.cat(self.raw_data['path_x'])
-        self.raw_data['path_y'] = torch.cat(self.raw_data['path_y'])
-
-        # pad second dimension of relative state
-        for i in range(len(self.raw_data['relative_state'])):
-            nv1, nv2, d = self.raw_data['relative_state'][i].shape
-            pad = torch.zeros(nv1, max_nv-nv2, d) * np.nan
-            self.raw_data['relative_state'][i] = torch.cat((self.raw_data['relative_state'][i], pad), dim=1)
-        self.raw_data['relative_state'] = torch.cat(self.raw_data['relative_state'])
-
-        # mandate equal length
-        assert len(self.raw_data['state']) == len(self.raw_data['relative_state']) \
-            == len(self.raw_data['action']) \
-            == len(self.raw_data['path_x']) \
-            == len(self.raw_data['path_y']), 'dataset lengths unequal'
-        
-    def __len__(self):
-        return len(self.raw_data['state'])
-
-    def __getitem__(self, idx):
-        """ 
-        Sample from the dataset
-        Args:
-            idx: index or indices of B samples
-        Returns:
-            sample (dict): sample dictionary with the following entries:
-                state (torch.tensor): (B, 5) raw state
-                relative_state (torch.tensor): (B, max_nv, d) relative state (padded with nans)
-                path_x (torch.tensor): (B, P) tensor of P future path x positions
-                path_y (torch.tensor): (B, P) tensor of P future path y positions
-                action (torch.tensor): (B, 1) actions taken from each state
-        """
-        keys = ['state', 'relative_state', 'path_x', 'path_y', 'action']
-        sample = {key:self.raw_data[key][idx] for key in keys}
-
-        for key in keys: 
-            if key in self.transforms.keys():
-                sample[key] = self.transforms[key](sample[key])
-
-        return sample

src/discriminator/__init__.py

@@ -0,0 +1 @@
+from src.discriminator.discriminator import *

src/discriminator/discriminator.py

@@ -0,0 +1,99 @@
+import torch
+
+# imitation.rewards.discrim_nets.DiscrimNetGAIL is composed of self.discriminator (nn.Module),
+# which gets called with inputs (state, action) when needed.
+
+class CnnDiscriminator(torch.nn.Module):
+    """ConvNet similar to stable_baselines3.common.policies.ActorCriticCnnPolicy."""
+
+    def __init__(self, env):
+        super().__init__()
+
+        obs_channels, _, _ = env.observation_space.shape
+        (action_size,) = env.action_space.shape
+        in_channels = obs_channels + action_size
+
+        self.cnn = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels, 4, kernel_size=8, stride=4, padding=0),
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(4, 8, kernel_size=4, stride=2, padding=0),
+            torch.nn.ReLU(),
+            torch.nn.Flatten(start_dim=-3, end_dim=-1),
+            torch.nn.LazyLinear(512),
+            torch.nn.ReLU(),
+            torch.nn.LazyLinear(1),
+        )
+    
+    @staticmethod
+    def _concatenate(state, action):
+        b, _, h, w = state.shape
+        _, a = action.shape
+        act = action.unsqueeze(-1).unsqueeze(-1).expand((b, a, h, w))
+        sa = torch.cat((state, act), -3)
+        return sa
+
+    def forward(self, state, action):
+        sa = self._concatenate(state, action)
+        assert sa.ndim == 4
+        return self.cnn(sa).squeeze(1)
+
+class CnnDiscriminatorFlatAction(torch.nn.Module):
+    """ConvNet similar to stable_baselines3.common.policies.ActorCriticCnnPolicy."""
+
+    def __init__(self, env):
+        super().__init__()
+
+        obs_channels, _, _ = env.observation_space.shape
+        (action_size,) = env.action_space.shape
+        in_channels = obs_channels
+
+        self.cnn = torch.nn.Sequential(
+            torch.nn.Conv2d(in_channels, 32, kernel_size=(8, 8), stride=(4, 4)), # in_channels -> 32
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2)), # 32 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)), # 64 -> 64
+            torch.nn.ReLU(),
+            torch.nn.Flatten(start_dim=1, end_dim=-1),
+            torch.nn.LazyLinear(128), # 28224 -> 128
+        )
+        self.decoder = torch.nn.Sequential(
+            torch.nn.LazyLinear(64), #128 + 2 -> 64
+            torch.nn.ReLU(),
+            torch.nn.LazyLinear(64), #64 -> 64
+            torch.nn.ReLU(),
+            torch.nn.LazyLinear(1) #64 -> 1
+        )
+    
+    @staticmethod
+    def _concatenate(state, action):
+        b, s= state.shape
+        b, a = action.shape
+        sa = torch.cat((state, action), -1)
+        return sa
+
+    def forward(self, state, action):
+        s = self.cnn(state.float())
+        sa = self._concatenate(s, action)
+        assert sa.ndim == 2
+        return self.decoder(sa).squeeze(1)
+
+class MlpDiscriminator(torch.nn.Module):
+    """MLP similar to stable_baselines3.common.policies.ActorCriticPolicy."""
+
+    def __init__(self, env=None):
+        super().__init__()
+        self.flatten = torch.nn.Flatten(start_dim=1, end_dim=-1)
+        self.mlp = torch.nn.Sequential(
+            torch.nn.LazyLinear(64), # 42 -> 64
+            torch.nn.Tanh(),
+            torch.nn.LazyLinear(64), # 64 -> 64
+            torch.nn.Tanh(),
+            torch.nn.LazyLinear(1),  # 64 -> 1
+        )
+    
+    def forward(self, state, action):
+        flat = self.flatten(state)
+        sa = torch.cat((action, flat), -1)
+        assert sa.ndim == 2
+        return self.mlp(sa).squeeze(1)

src/discriminator/test_discriminator.py

@@ -0,0 +1,45 @@
+from intersim.envs.intersimple import NRasterized
+from discriminator import CnnDiscriminator
+import torch
+
+def test_image_concatenation():
+    env = NRasterized()
+    disc = CnnDiscriminator(env)
+    s = torch.tensor(env.reset()).unsqueeze(0)
+    a = torch.tensor([[0.5]])
+    sa = disc._concatenate(s, a)
+
+    assert s.shape == (1, 5, 200, 200)
+    assert a.shape == (1, 1)
+    assert sa.shape == (1, 6, 200, 200)
+    assert torch.allclose(sa[:, :5], 1.0 * s)
+    assert (sa[:, 5] == a.unsqueeze(-1)).all()
+
+def test_image_concatenation3():
+    env = NRasterized()
+    disc = CnnDiscriminator(env)
+
+    s1 = env.reset()
+    a1 = 0.15
+    s2, _, _, _ = env.step(0.9)
+    a2 = 0.25
+    s3, _, _, _ = env.step(-0.9)
+    a3 = 0.35
+    
+    s = torch.stack([
+        torch.tensor(s1),
+        torch.tensor(s2),
+        torch.tensor(s3)
+    ], axis=0)
+    a = torch.tensor([
+        [a1],
+        [a2],
+        [a3],
+    ])
+    sa = disc._concatenate(s, a)
+
+    assert s.shape == (3, 5, 200, 200)
+    assert a.shape == (3, 1)
+    assert sa.shape == (3, 6, 200, 200)
+    assert torch.allclose(sa[:, :5], 1.0 * s)
+    assert (sa[:, 5] == a.unsqueeze(-1)).all()

src/evaluation/evaluation.py

@@ -0,0 +1,93 @@
+import torch
+import numpy as np
+from stable_baselines3.common.vec_env import VecEnv
+from stable_baselines3.common.evaluation import evaluate_policy
+
+from intersim.envs.intersimple import Intersimple
+from src.evaluation.metrics import nanmean, divergence, visualize_distribution
+import os
+
+
+class Evaluation:
+    def __init__(self, filestr, eval_env, expert_data, n_eval_episodes=10):
+        # if env is a VecEnv, the code needs to be adapted, since the callback will be called after each step, 
+        # so transitions of different envs will be mixed and the total number of episodes could be larger than n_eval_episodes!
+        assert not isinstance(eval_env, VecEnv)
+        self.filestr = filestr
+        self.env = eval_env
+        self.n_eval_episodes = n_eval_episodes
+        self.expert_data = expert_data
+        self.compute_expert_features(expert_data)
+        self.reset()
+
+    def reset(self):
+        self._n_collisions = 0
+        self._trajectories = []
+        self._episode_done = True
+        self._accelerations = []
+
+    def compute_expert_features(self, expert_data):
+        # expert velocities
+        extract_state = lambda info: info['projected_state'][info['agent']]
+        expert_velocities = torch.stack([extract_state(info) for info in expert_data.infos])[:,2]
+        self.expert_velocities = expert_velocities[~torch.isnan(expert_velocities)]
+        # expert accelerations
+        extract_accel = lambda info: info['action_taken'][info['agent']]
+        self.expert_accelerations = torch.cat([extract_accel(info) for info in expert_data.infos])
+
+    def evaluate(self, epoch, generator, discriminator):
+        self.reset()
+        metrics = {}
+        
+        episode_rewards, episode_lengths = evaluate_policy(
+            generator, 
+            self.env,
+            n_eval_episodes=self.n_eval_episodes,
+            callback=self.evaluate_policy_callback, 
+            return_episode_rewards=True
+        )
+
+        collision_rate = self._n_collisions / self.n_eval_episodes
+        metrics['collision_rate'] = collision_rate
+
+        assert len(self._trajectories) >= self.n_eval_episodes
+
+        # velocities produced by generator
+        policy_velocities = torch.cat([torch.stack(t)[:,2] for t in self._trajectories])
+        # if episodes terminate without collisions, then the state is fully nan
+        policy_velocities = policy_velocities[~torch.isnan(policy_velocities)]
+
+        metrics['avg_velocity_loss'] = (self.expert_velocities.mean() - policy_velocities.mean()).item()
+        metrics['velocity_divergence'] = divergence(policy_velocities, self.expert_velocities, type='js')
+        
+
+        # accelerations produced by generator
+        policy_accelerations = torch.tensor(self._accelerations)
+
+        metrics['acceleration_divergence'] = divergence(policy_accelerations, self.expert_accelerations, type='js')
+        visualize_distribution(self.expert_accelerations, policy_accelerations, os.path.join(self.filestr, '_action_viz{:02}'.format(epoch))) 
+
+        print(metrics)
+        return metrics
+
+    def evaluate_policy_callback(self, local_vars, global_vars):
+        venv_i = local_vars['i']
+        info = local_vars['info']
+        done = local_vars['done']
+        _agent = info['agent']
+        env = local_vars['env'].envs[venv_i]
+        assert isinstance(env, Intersimple)
+
+        # Increase collision counter if episode terminated with a collision
+        if info['collision']:
+            assert done
+            self._n_collisions += 1
+
+        # if last episode is done, start new trajectory
+        # this is currently not necessary, only if velocity is to be averaged over individual trajectories first
+        # and then averaging over all trajectories
+        if self._episode_done:
+            self._trajectories.append([])
+        self._trajectories[-1].append(info['projected_state'][_agent])
+        self._accelerations.append(info['action_taken'][_agent])
+        self._episode_done = done

src/evaluation/metrics.py

@@ -0,0 +1,251 @@
+import torch
+import pickle
+import numpy as np
+import matplotlib.pyplot as plt
+from torch.utils.data import DataLoader
+from intersim import collisions
+
+def metrics(filestr: str, test_dataset, policy):
+    """
+    Calculate metrics using a) base filestring to a simulation, and b) the test dataset and learned policy
+    Args:
+        filestr (str): base string to outputs of a simulation
+        test_dataset: a dataset held for testing
+        policy: policy
+    Returns:
+        info (dict): metrics in a dictionary
+    """
+    info = {}
+
+    # compute metrics using either
+    # a) simulation files that were saved under the trained policy with prefix 'policy'
+    # b) applying the policy to observations in the test dataset
+
+    # load simulated trajectory
+    states = torch.load(filestr + '_sim_states.pt').detach()
+    lengths = torch.load(filestr + '_sim_lengths.pt').detach()
+    widths = torch.load(filestr + '_sim_widths.pt').detach()
+    xpoly = torch.load(filestr + '_sim_xpoly.pt').detach()
+    ypoly = torch.load(filestr + '_sim_ypoly.pt').detach()
+
+    # count collisions (from function in intersim.collisions)
+    n_collisions = collisions.count_collisions_trajectory(states, lengths, widths)
+    info['n_collisions'] = n_collisions
+
+    # calculate average velocity
+    avg_v = average_velocity(states)
+    info['average_velocity'] = avg_v
+
+    # convert policy dtype between float32 and float64
+    policy.policy = policy.policy.type(test_dataset[0]['state']['ego_state'].dtype)
+    
+    # generate actions in test dataset
+    true_actions, pred_actions = [], []
+    true_velocities = []
+    test_loader = DataLoader(test_dataset, batch_size=1024)
+    with torch.no_grad():
+        for (batch_idx, batch) in enumerate(test_loader):
+            pred_actions.append(policy(batch['state']))
+            true_actions.append(batch['action'])
+            true_velocities.append(batch['state']['ego_state'][:,2])
+
+    true_actions, pred_actions = torch.cat(true_actions,dim=0), torch.cat(pred_actions, dim=0)
+    visualize_distribution(true_actions[:,0], pred_actions[:,0], filestr+'_action_viz') 
+
+    # calculate divergence between acceleration distributions
+    acceleration_divergence = divergence(pred_actions, true_actions, type='js')
+    info['acceleration_divergence'] = acceleration_divergence
+
+    # calculate divergence between velocity distributions
+    sim_velocities = states[:,:,2]
+    sim_velocities = sim_velocities[~torch.isnan(sim_velocities)].flatten()
+    true_velocities = torch.cat(true_velocities, dim=0)
+    velocity_divergence = divergence(sim_velocities, true_velocities, type='js')
+    info['velocity_divergence'] = velocity_divergence
+
+    return info
+
+
+def visualize_distribution(true, pred, filestr):
+    """
+    Visualize two distributions
+    Args:
+        true (torch.tensor): (n,)-sized true distribution
+        pred (torch.tensor): (m,)-sized pred distribution
+        filestr (str): string to save figure to
+    """
+    nni1 = ~torch.isnan(true)
+    nni2 = ~torch.isnan(pred)
+    plt.figure()
+    plt.hist(true[nni1].numpy(), density=True, bins=20)
+    plt.hist(pred[nni2].numpy(), density=True, bins=20)
+    plt.legend(['True', 'Predicted'])
+    plt.savefig(filestr+'.png')
+    
+def average_velocity(states):
+    """
+    Compute average of average velocity over all vehicles.
+    Args:
+        states (torch.tensor): (T,nv,5) vehicle states where T is the number of time steps and nv the number of vehicles
+    Returns
+        avg_v (float): average velocity
+    """
+    velocities = states[:,:,2]
+    # average velocity per vehicle
+    vehicle_avg_v = nanmean(velocities, dim=0)
+    arg_v = nanmean(vehicle_avg_v)
+    return arg_v
+
+def divergence(p, q, type='js', n_components=-1):
+    """
+    Calculate a divergence between p and q
+    Args:
+        p (torch.tensor): (n) samples from p
+        q (torch.tensor): (m) samples from q
+        type (str): divergence to use
+            'kl': Kullback-Leibler divergence KL(p||q)
+            'js': Jensen-Shannon divergence (symmetric KLD)
+        n_components (int): method to use to compute kl divergence
+            n_components < 0: approximate samples with histogram density
+            n_components == 0: approximate samples by Gaussian distributions and compute analytically
+            n_components > 0: approximate samples as Gaussian mixture models with n_components components
+    Returns:
+        d (float): approximate divergence
+    """
+    if type == 'js':
+        # Use histogram binning to discretize sampled distributions
+        p_hist = np.histogram(p, bins='auto', density=True)
+        q_hist = np.histogram(q, bins='auto', density=True)
+        m = torch.cat([p, q], dim=0)
+        m_weights = torch.cat([torch.full_like(p, 1./len(p)), torch.full_like(q, 1./len(q))], dim=0)
+        m_bins = np.sort(np.concatenate([p_hist[1], q_hist[1]]))
+        m_hist = np.histogram(m, bins=m_bins, density=True, weights=m_weights)
+        d = .5 * kl_histogram(p, p_hist, m_hist) + .5 * kl_histogram(q, q_hist, m_hist)
+        return d
+    elif type == 'kl':
+        if n_components < 0:
+            # Use histogram binning to discretize sampled distributions
+            p_hist = np.histogram(p, bins='auto', density=True)
+            q_hist = np.histogram(q, bins='auto', density=True)
+            d = kl_histogram(p, p_hist, q_hist)
+            return d
+        elif n_components == 0:
+            # Assume p and q to be Gaussian
+            pm = torch.mean(p)
+            qm = torch.mean(q)
+            pv = torch.var(p)
+            qv = torch.var(q)
+            d = kl_normal(pm, pv, qm, qv).item()
+            return d
+        else:
+            from sklearn.mixture import GaussianMixture
+            p = p.unsqueeze(-1)
+            q = q.unsqueeze(-1)
+            p_gmm = GaussianMixture(n_components=n_components).fit(p)
+            q_gmm = GaussianMixture(n_components=n_components).fit(q)
+            px = p_gmm.score_samples(p)
+            qx = q_gmm.score_samples(p)
+            d = np.mean(px - qx).item()
+            return d
+    else:
+        raise NotImplementedError("Please implement divergence for type '{}'".format(type))
+
+def kl_histogram(p_sample, p_hist, q_hist):
+    """
+    Calculate the kl divergence between p and q based on a histogram representation
+    Args:
+        p_sample (torch.tensor): (n) samples from p
+        p_hist (tuple): result of np.histogram(density=True) for samples from p
+        q_hist (tuple): result of np.histogram(density=True) for samples from q
+    Returns:
+        d (float): approximate KL divergence
+    """
+    p_density, p_edges = p_hist
+    q_density, q_edges = q_hist
+    px = evaluate_histogram(p_sample, p_density, p_edges)
+    qx = evaluate_histogram(p_sample, q_density, q_edges)
+    p_supp = ~np.isclose(px, 0.0)
+    q_supp = ~np.isclose(qx, 0.0)
+    if np.any(np.logical_and(p_supp, ~q_supp)):
+        # if not support(p) subset support(q)
+        return np.inf
+    elif ~np.any(p_supp):
+        # if p is zero everywhere
+        return 0.
+    d = np.mean(np.log(px[p_supp] / qx[p_supp]))
+    return d
+
+
+def kl_normal(pm, pv, qm, qv):
+    """
+    Computes the elem-wise KL divergence between two normal distributions KL(p || q) and
+    sum over the last dimension
+
+    Args:
+        pm: tensor: (batch, dim): p mean
+        pv: tensor: (batch, dim): p variance
+        qm: tensor: (batch, dim): q mean
+        qv: tensor: (batch, dim): q variance
+
+    Return:
+        kl: tensor: (batch,): kl between each sample
+    """
+    element_wise = 0.5 * (torch.log(qv) - torch.log(pv) + pv / qv + (pm - qm).pow(2) / qv - 1)
+    kl = element_wise.sum(-1)
+    return kl
+
+
+def kl_cat(q, log_q, log_p):
+    """
+    Computes the KL divergence between two categorical distributions
+
+    Args:
+        q: tensor: (batch, dim): Categorical distribution parameters
+        log_q: tensor: (batch, dim): Log of q
+        log_p: tensor: (batch, dim): Log of p
+
+    Return:
+        kl: tensor: (batch,) kl between each sample
+    """
+    element_wise = (q * (log_q - log_p))
+    kl = element_wise.sum(-1)
+    return kl
+
+
+def nanmean(v, *args, inplace=False, **kwargs):
+    """
+    Calculate mean over not nan entries
+
+    To be added to torch as torch.nanmean in the next release
+    https://github.com/pytorch/pytorch/issues/61474, https://github.com/pytorch/pytorch/issues/21987
+
+    Args:
+        v (torch.tensor): arbitrary tensor
+    Returns:
+        result (torch.tensor): mean over non nan elements
+    """
+    if not inplace:
+        v = v.clone()
+    is_nan = torch.isnan(v)
+    v[is_nan] = 0
+    result = v.sum(*args, **kwargs) / (~is_nan).float().sum(*args, **kwargs)
+    return result
+
+
+def evaluate_histogram(x, hist, bin_edges):
+    """
+    Evaluate a histogram 
+    Args:
+        x (array) : points at which to evaluate the histogram
+        hist (array): histogram values in terms of number of occurrences or probability
+        bin_edges (array): edges of histogram bins
+            e.g. from hist, bin_edges = np.histogram(p, bins='auto', density=True)
+    Return:
+        r: tensor: (batch,) kl between each sample
+    """
+    idx = np.digitize(x, bin_edges)
+    mask = np.logical_and(np.less(0, idx), np.less(idx, len(bin_edges)))
+    r = np.zeros_like(x)
+    r[mask] = hist[idx[mask] - 1]
+    return r
+

src/expert_data.py

@@ -1,95 +0,0 @@
-import torch
-
-import pickle
-import gym
-import numpy as np
-
-import intersim
-from intersim.utils import get_map_path, get_svt, SVT_to_stateactions
-from intersim import collisions
-import os
-opj = os.path.join
-
-def generate_expert_data(path: str='expert_data', loc: int = 0, track:int = 0, **kwargs):
-    """
-    Function to save (joint) states and observations from simulated frame
-    Args:
-        path (str): directory to save data
-        loc (int): location index
-        track (int): track index
-        kwargs: arguments for environment instantiation
-    """
-    
-    if not os.path.isdir(path):
-        os.mkdir(path)
-    filestr = opj(path,intersim.LOCATIONS[loc]+'_track%03i'%(track))
-    
-    svt, svt_path = get_svt(base='InteractionSimulator', loc=loc, track=track)
-    osm = get_map_path(base='InteractionSimulator', loc=loc)
-    print('SVT path: {}'.format(svt_path))
-    print('Map path: {}'.format(osm))
-    states, actions = SVT_to_stateactions(svt)
-
-    # animate from environment
-    env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, **kwargs, 
-        min_acc=-np.inf, max_acc=np.inf)
-    
-    env.reset()
-    done = False
-    obs, actions_taken, max_devs = [], [], []
-    i = 0
-    while not done and i < len(actions):
-        # check state deviation
-        env_state = env.projected_state
-        nni = ~torch.isnan(env_state[:,0])
-        norms = torch.norm(env_state[nni,:2]-states[i,nni,:2], dim=1)
-        max_devs.append(norms.max())
-
-        # propagate environment
-        ob, r, done, info = env.step(env.target_state(svt.simstate[i+1]))
-        obs.append(ob)
-        actions_taken.append(info['action_taken'])
-        i += 1
-    
-    print("Maximum environment deviation from track: %f m" %(max(max_devs)))
-
-    # check for collisions
-    x = torch.stack([ob['state'] for ob in obs])
-    cols = collisions.check_collisions_trajectory(x, svt.lengths, svt.widths)
-    assert ~torch.any(cols), 'Error: Collisions found at indices {}'.format(cols.nonzero(as_tuple=True))
-
-    # shift actions
-    actions_taken.pop(0)
-    obs.pop(-1)
-
-    # save observations and actions
-    pickle.dump(obs,open(filestr+'_observations.pkl', 'wb'))
-    torch.save(torch.stack(actions_taken), filestr+'_actions.pt')
-
-def load_expert_data(path='expert_data', loc: int = 0, track:int = 0):
-    """
-    Load expert data from file.
-    Args:
-        path (str): directory to save data
-        loc (int): location index
-        track (int): track index
-    Returns:
-        obs (list[Observations]): list of observations
-        actions (list[torch.tensor]): list of corresponding actions taken in observations
-    """
-    # load observations and actions
-    filestr = opj(path, intersim.LOCATIONS[loc]+'_track%03i'%(track))
-    obs = pickle.load(open(filestr+'_observations.pkl', 'rb'))
-    actions = torch.load(filestr+'_actions.pt')
-    actions = list(torch.unbind(actions))
-    return obs, actions
-
-if __name__ == '__main__':
-    import argparse
-    parser = argparse.ArgumentParser(description='Save Expert Trajectories')
-    parser.add_argument('--loc', default=0, type=int,
-                       help='location (default 0)')
-    parser.add_argument('--track', default=0, type=int,
-                       help='track number (default 0)')
-    args = parser.parse_args()
-    generate_expert_data(loc=args.loc,track=args.track)

src/gail/options.py

@@ -0,0 +1,165 @@
+import gym
+import torch
+from src.util.collisions import feasible
+import numpy as np
+
+class OptionsEnv(gym.Wrapper):
+    
+    def __init__(self, env, options=[(0, 5), (5, 5), (10, 5)], *args, **kwargs):
+        """option 0 is treated as safe fallback"""
+
+        super().__init__(env, *args, **kwargs)
+        self.options = options
+        num_hl_options = len(self.options)
+        self.action_space = gym.spaces.Discrete(num_hl_options)
+        self.observation_space = gym.spaces.Dict({
+            'obs': env.observation_space,
+            'mask': gym.spaces.Box(low=0, high=1, shape=(num_hl_options,)),
+        })
+
+    def _after_choice(self):
+        pass
+    
+    def _after_step(self):
+        pass
+
+    def _transitions(self):
+        raise NotImplementedError('Use `LLOptions` or `HLOptions` for sampling.')
+    
+    def sample(self, generator):
+        self.done = True
+        while True:
+            self.episode_start = False
+            if self.done:
+                self.s = self.env.reset()
+                self.done = False
+                self.episode_start = True
+
+            self.m = available_actions(self.env, self.options)
+            if not self.m.any():
+                # action 0 is considered safe fallback
+                self.m[0] = True
+            
+            self.ch, self.value, self.log_prob = generator.policy.forward({
+                'obs': torch.tensor(self.s).unsqueeze(0).to(generator.policy.device),
+                'mask': self.m.unsqueeze(0).to(generator.policy.device),
+            })
+            self.plan = list(map(float, generate_plan(self.env, self.ch, self.options)))
+
+            self._after_choice()
+
+            assert not self.done
+            assert self.plan
+            #assert feasible(self.env, self.plan, self.ch)
+
+            while not self.done and self.plan and \
+                (feasible(self.env, safety_plan(self.env, self.plan)) or self.m.sum() == 1):
+                
+                self.a, self.plan = self.plan[0], self.plan[1:]
+                self.a = self.env._normalize(self.a)
+                self.nexts, _, self.done, _ = self.env.step(self.a)
+
+                self._after_step()
+
+                self.s = self.nexts
+            
+            yield from self._transitions()
+
+class LLOptions(OptionsEnv):
+    """Sample low-level (state, action) tuples for discriminator training."""
+    
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.observation_space = self.observation_space['obs']
+
+    def _after_choice(self):
+        self._transition_buffer = []
+
+    def _after_step(self):
+        self._transition_buffer.append({
+            'obs': self.s,
+            'next_obs': self.nexts,
+            'acts': np.array((self.a,)),
+            'dones': np.array(self.done),
+        })
+
+    def _transitions(self):
+        yield from self._transition_buffer
+    
+    def sample_ll(self, policy):
+        return self.sample(policy)
+
+class HLOptions(OptionsEnv):
+    """Sample high-level (state, action, reward) tuples for generator training."""
+    
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def _after_choice(self):
+        self.obs = {'obs': np.copy(self.s), 'mask': np.copy(self.m)}
+        self.r = 0
+        self.steps = 0
+
+    def _after_step(self):
+        self.r += self.discount**self.steps * self.discriminator.discrim_net.predict_reward_train(
+            state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()),
+            action=torch.tensor([[self.a]]).to(self.discriminator.discrim_net.device()),
+            next_state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
+            done=torch.tensor(self.done).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
+        )
+        self.steps += 1
+    
+    def _transitions(self):
+        yield {
+            'obs': self.obs,
+            'action': self.ch.cpu(),
+            'reward': self.r,
+            'episode_start': self.episode_start,
+            'value': self.value.detach(),
+            'log_prob': self.log_prob.detach(),
+            'done': self.done,
+        }
+    
+    def sample_hl(self, policy, discriminator):
+        self.discriminator = discriminator
+        return self.sample(policy)
+
+class RenderOptions(LLOptions):
+
+    def _after_step(self):
+        super()._after_step()
+        self.env.render()
+    
+    def close(self, *args, **kwargs):
+        self.env.close(*args, **kwargs)
+
+def safety_plan(env, plan):
+    return np.concatenate((plan, np.array(5 * [env._env._min_acc])), axis=0)
+
+def available_actions(env, options):
+    """Return mask of available actions given current `env` state."""
+    plans = [generate_plan(env, i, options) for i, _ in enumerate(options)]
+    # is emergency braking still possible?
+    plans = list(map(lambda p: safety_plan(env, p), plans))
+
+    T = max(len(p) for p in plans)
+    plans = [np.pad(p, ((0, T-len(p)),), constant_values=np.nan) for p in plans]
+    plans = np.stack(plans, axis=0)
+    
+    valid = feasible(env, plans)
+    return valid
+
+def target_velocity_plan(current_v: float, target_v: float, t: int, dt: float):
+    """Smoothly target a velocity in a given number of steps"""
+    # for now, constant acceleration
+    a = (target_v - current_v)  / (t * dt)
+    return a*np.ones((t,))
+
+def generate_plan(env, i, options):
+    """Generate input profile for high-level action `i`."""
+    assert i < len(options), "Invalid option index {i}"
+    target_v, t = options[i]
+    current_v = env._env.state[env._agent, 1].item() # extract from env
+    plan = target_velocity_plan(current_v, target_v, t, env._env._dt)
+    assert len(plan) == t, "incorrect plan length"
+    return plan

src/gail/test_options.py

@@ -0,0 +1,59 @@
+import pickle
+import imitation.data.rollout as rollout
+from options import LLOptions, OptionsEnv
+from intersim.envs import NRasterized
+import itertools
+import stable_baselines3
+from policy import OptionsCnnPolicy
+from train import flatten_transitions
+import numpy as np
+
+def test_ll_expert_data():
+    with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
+        expert_trajectories = pickle.load(f)
+    expert_transitions = rollout.flatten_trajectories(expert_trajectories)
+
+    env = LLOptions(NRasterized(agent=51, width=36, height=36, m_per_px=2))
+
+    gen_transitions = list(itertools.islice(env.sample_ll(
+        policy=stable_baselines3.PPO(
+            OptionsCnnPolicy,
+            OptionsEnv(env),
+            verbose=1,
+        )
+    ), 10))
+    gen_transitions = flatten_transitions(gen_transitions)
+
+    assert expert_transitions[:10].obs.shape == gen_transitions['obs'].shape
+    assert expert_transitions[:10].next_obs.shape == gen_transitions['next_obs'].shape
+    assert expert_transitions[:10].acts.shape == gen_transitions['acts'].shape
+    assert expert_transitions[:10].dones.shape == gen_transitions['dones'].shape
+
+def test_ll_states():
+    env = NRasterized()
+    policy = stable_baselines3.PPO(
+        OptionsCnnPolicy,
+        OptionsEnv(env),
+        verbose=1,
+    )
+    llenv = LLOptions(env)
+    transitions = list(itertools.islice(llenv.sample_ll(policy=policy), 100))
+
+    env2 = NRasterized()
+    s2 = env2.reset()
+    for i, t in enumerate(transitions):
+        assert i == 0 or np.array_equal(t['obs'], transitions[i-1]['next_obs'])
+        assert np.array_equal(t['obs'], s2)
+        assert t['acts'].shape == (1,)
+
+        nexts2, _, done2, _ = env2.step(t['acts'])
+        assert np.array_equal(t['next_obs'], nexts2)
+        assert np.array_equal(t['dones'], done2)
+
+        if done2:
+            break
+
+        s2 = nexts2
+
+def test_hl_transitions():
+    pass

src/gail/train.py

@@ -0,0 +1,37 @@
+import numpy as np
+import itertools
+
+def flatten_transitions(transitions):
+    return {
+        'obs': np.stack(list(t['obs'] for t in transitions), axis=0),
+        'next_obs': np.stack(list(t['next_obs'] for t in transitions), axis=0),
+        'acts': np.stack(list(t['acts'] for t in transitions), axis=0),
+        'dones': np.stack(list(t['dones'] for t in transitions), axis=0),
+    }
+
+def train_discriminator(env, generator, discriminator, num_samples, n_updates=1):
+    transitions = list(itertools.islice(env.sample_ll(generator), num_samples))
+    generator_samples = flatten_transitions(transitions)
+    for _ in range(n_updates):
+        discriminator.train_disc(gen_samples=generator_samples)
+
+def train_generator(env, generator, discriminator, num_samples):
+    generator_samples = list(itertools.islice(env.sample_hl(generator, discriminator), num_samples+1))
+    
+    generator.rollout_buffer.reset()
+    for s in generator_samples[:-1]:
+        generator.rollout_buffer.add(
+            obs=s['obs'],
+            action=s['action'],
+            reward=s['reward'],
+            episode_start=s['episode_start'],
+            value=s['value'],
+            log_prob=s['log_prob'],
+        )
+    
+    generator.rollout_buffer.compute_returns_and_advantage(
+        last_values=generator_samples[-1]['value'],
+        dones=generator_samples[-1]['done'],
+    )
+    
+    generator.train()