Scratch for horner scheme

Better way would probably be to rewrite flat_grad and reparam

.gitignore

@@ -1,6 +1,8 @@
 *.pkl
 *.pt
 *.zip
+**/ray/*
+**/runs/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -149,3 +151,4 @@ output/
 
 # Dependencies
 InteractionSimulator/
+imitation/

evaluate_models.sh

@@ -0,0 +1,42 @@
+# eval_main inputs
+# locations: List[Tuple[int,int]]= [(0,0)],
+# method: str='expert', 
+# policy_file: str='', 
+# policy_kwargs: dict={},
+# env: str='NRasterizedRouteIncrementingAgent', 
+# env_kwargs: dict={},
+# seed: int=0
+
+# expert
+python -m src.eval_main
+
+# idm
+python -m src.eval_main --method=idm
+
+# behavior cloning
+python -m src.eval_main --method=bc --policy_file='checkpoints/bc-intersimple-setobs2.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=0
+python -m src.eval_main --method=bc --policy_file='checkpoints/bc-intersimple-setobs2.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=1
+python -m src.eval_main --method=bc --policy_file='checkpoints/bc-intersimple-setobs2.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=2
+python -m src.eval_main --method=bc --policy_file='checkpoints/bc-intersimple-setobs2.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=3
+python -m src.eval_main --method=bc --policy_file='checkpoints/bc-intersimple-setobs2.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=4
+python -m src.evaluation.utils load_and_average out/bc
+
+# GAIL
+python -m src.eval_main --method=gail --policy_file='checkpoints/gail-intersimple-setobs2-03-02-22.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=0
+python -m src.eval_main --method=gail --policy_file='checkpoints/gail-intersimple-setobs2-03-02-22.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=1
+python -m src.eval_main --method=gail --policy_file='checkpoints/gail-intersimple-setobs2-03-02-22.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=2
+python -m src.eval_main --method=gail --policy_file='checkpoints/gail-intersimple-setobs2-03-02-22.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=3
+python -m src.eval_main --method=gail --policy_file='checkpoints/gail-intersimple-setobs2-03-02-22.pt' --env='NormalizedContinuousEvalEnv' --env_kwargs='{stop_on_collision:True}' --seed=4
+python -m src.evaluation.utils load_and_average out/gail
+
+# options GAIL
+python -m src.eval_main --method=ogail --policy_file='checkpoints/gail-options-setobs2-Feb15_18-49-05.pt' --env='NormalizedOptionsEvalEnv' --env_kwargs='{stop_on_collision:True}'
+
+# options GAIL-PPO
+python -m src.eval_main --method=ogail-ppo --policy_file='checkpoints/gail-ppo-options-setobs2-Feb15_22-05-38.pt' --env='NormalizedOptionsEvalEnv' --env_kwargs='{stop_on_collision:True}'
+
+# SHAIL
+python -m src.eval_main --method=sgail --policy_file='checkpoints/sgail-options-setobs2.pt' --env='NormalizedSafeOptionsEvalEnv' --env_kwargs='{stop_on_collision:True,max_episode_steps:1000}'
+
+# SHAIL-PPO
+python -m src.eval_main --method=sgail-ppo --policy_file='checkpoints/sgail-ppo-options-setobs2-17-02-2022.pt' --env='NormalizedSafeOptionsEvalEnv' --env_kwargs='{stop_on_collision:True,max_episode_steps:1000}'

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

@@ -4,6 +4,7 @@ sklearn
 pytest
 json5
 tqdm
-tensorboard
 ray[tune]
-hyperopt
+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

@@ -36,7 +36,49 @@ class CnnDiscriminator(torch.nn.Module):
 
     def forward(self, state, action):
         sa = self._concatenate(state, action)
-        return self.cnn(sa).squeeze()
+        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."""
@@ -55,4 +97,5 @@ class MlpDiscriminator(torch.nn.Module):
     def forward(self, state, action):
         flat = self.flatten(state)
         sa = torch.cat((action, flat), -1)
-        return self.mlp(sa).squeeze()
+        assert sa.ndim == 2
+        return self.mlp(sa).squeeze(1)

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/etienne/intersimple/data/expert.py

@@ -1,7 +1,8 @@
-from intersim.envs.intersimple import Intersimple
+from intersim.envs.intersimple import Intersimple, InfoFilter
 from stable_baselines3.common.policies import BasePolicy
 import gym
-import intersim.envs.intersimple
+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
@@ -49,6 +50,8 @@ class IntersimpleExpert(BasePolicy):
         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):
@@ -63,16 +66,28 @@ class NormalizedIntersimpleExpert(IntersimpleExpert):
         action, _ = super().predict(*args, **kwargs)
         return self._intersimple._normalize(action), None
 
-class DummyVecEnvPolicy():
+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()
@@ -91,7 +106,7 @@ def demonstrations(expert='NormalizedIntersimpleExpert', env='NRasterizedRandomA
         python -m intersimple.expert <flags>
 
     """
-    Env = intersim.envs.intersimple.__dict__[env]
+    Env = globals()[env]
     Expert = globals()[expert]
 
     env = Env(**env_args)
@@ -105,6 +120,7 @@ def demonstrations(expert='NormalizedIntersimpleExpert', env='NRasterizedRandomA
         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,
@@ -113,7 +129,8 @@ def demonstrations(expert='NormalizedIntersimpleExpert', env='NRasterizedRandomA
         sample_until=rollout.make_sample_until(
             min_timesteps=min_timesteps,
             min_episodes=min_episodes,
-        )
+        ),
+        exclude_infos=not include_infos,
     )
 
 if __name__ == '__main__':

scratch/etienne/intersimple/data/generate.sh

@@ -1,4 +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=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_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_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

@@ -1,5 +1,8 @@
 # %%
-from gail.discriminator import CnnDiscriminator
+import sys
+sys.path.append('../../../')
+
+from src.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
 from imitation.algorithms import adversarial
 import stable_baselines3
 import torch.utils.data
@@ -15,198 +18,17 @@ 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, 20]] # option 0 is safe fallback
+ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10]] # option 0 is safe fallback
 
-class OptionsCnnPolicy(stable_baselines3.common.policies.ActorCriticCnnPolicy):
+def train(expert_data, epochs=20, expert_batch_size=32, generator_steps=1024, discount=0.99):
-
-    def __init__(self, observation_space, *args, **kwargs):
-        super().__init__(observation_space['obs'], *args, **kwargs)
-
-    def _prior_distribution(self, s):
-        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):
-        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):
-        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
-
-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 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
-    return ch == 0 or valid.item()
-
-def sample(env, generator, discriminator, level: str):
-    """
-        Sample low-level (state, action, next_state) tuples for discriminator training or 
-        high-level (state, action, reward) tuples for generator training.
-    """
-    done = True
-    while True:
-        episode_start = False
-        if done:
-            s = env.reset()
-            m = available_actions(env)
-            done = False
-            episode_start = True
-
-        obs = {'obs': s, 'mask': m}
-        ch, value, log_prob = generator.policy.predict({
-            'obs': torch.tensor(s).unsqueeze(0).to(generator.policy.device),
-            'mask': torch.tensor(m).unsqueeze(0).to(generator.policy.device),
-        })
-        plan = list(map(float, generate_plan(env, ch)))
-
-        assert not done
-        assert plan
-        assert feasible(env, plan, ch), f'Infeasible hl action {ch}'
-
-        r = 0
-        discount = 1
-        while not done and plan and feasible(env, plan, ch):
-            a, plan = env._normalize(plan[0]), plan[1:]
-            if level == 'high':
-                r += discount * discriminator.discrim_net.discriminator(
-                    torch.tensor(s).unsqueeze(0).to(discriminator.discrim_net.device()),
-                    torch.tensor([[a]]).to(discriminator.discrim_net.device()),
-                )
-                discount *= env.discount
-
-            nexts, _, done, _ = env.step(a)
-            m = available_actions(env)
-
-            if level == 'low':
-                yield {
-                    'obs': s,
-                    'next_obs': nexts,
-                    'acts': np.array((a,)),
-                    'dones': np.array(done),
-                }
-            s = nexts
-
-        if level == 'high':
-            yield {
-                'obs': obs,
-                'option': ch,
-                'reward': r.detach(),
-                'episode_start': episode_start,
-                'value': value.detach(),
-                'log_prob': log_prob.detach(),
-                'done': done,
-            }
-
-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(sample(env, generator, None, 'low'), 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(sample(env, generator, discriminator, 'high'), num_samples+1))
-    
-    generator.rollout_buffer.reset()
-    for s in generator_samples[:-1]:
-        generator.rollout_buffer.add(
-            obs=s['obs'],
-            action=s['option'].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()
-
-class OptionsEnv(gym.Wrapper):
-    
-    def __init__(self, env):
-        super().__init__(env)
-        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 train(expert_data, epochs=10, expert_batch_size=32, generator_steps=2048, discount=0.99):
     env = NRasterized(**env_settings)
     env.discount = discount
 
@@ -219,14 +41,15 @@ def train(expert_data, epochs=10, expert_batch_size=32, generator_steps=2048, di
     discriminator = adversarial.GAIL(
         expert_data=expert_data,
         expert_batch_size=expert_batch_size,
-        discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
+        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),
+        OptionsEnv(env, options=ALL_OPTIONS),
         verbose=1,
         n_steps=generator_steps,
     )
@@ -238,60 +61,30 @@ def train(expert_data, epochs=10, expert_batch_size=32, generator_steps=2048, di
         generator.tensorboard_log,
     )
 
-    for _ in range(epochs):
+    for _ in tqdm(range(epochs)):
-        train_discriminator(env, generator, discriminator, num_samples=expert_batch_size)
+        train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size)
-        train_generator(env, generator, discriminator, num_samples=generator_steps)
+        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_steps=200)
+    generator = train(transitions)
 
     generator.save(model_name)
 
     # %%
     model = stable_baselines3.PPO.load(model_name)
 
-    env = NRasterized(**env_settings)
+    env = RenderOptions(NRasterized(**env_settings), options=ALL_OPTIONS)
 
-    for transition in sample(env, generator, None, 'low'):
+    for s in env.sample_ll(model):
-        env.render()
+        if s['dones']:
-        if transition['dones']:
             break
 
     env.close(filestr='render/'+model_name)
-
-# %% Tests
-
-def test_ll_transitions_vs_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 = NRasterized(agent=51, width=36, height=36, m_per_px=2)
-
-    gen_transitions = list(itertools.islice(sample(
-        env=NRasterized(**env_settings),
-        generator=stable_baselines3.PPO(
-            OptionsCnnPolicy,
-            OptionsEnv(env),
-            verbose=1,
-        ),
-        discriminator=None,
-        level='low'
-    ), 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_hl_transitions():
-    pass

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/ppo_speed_lidar.py

@@ -0,0 +1,50 @@
+# %%
+from stable_baselines3 import PPO
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+
+model_name = "ppo_speed_lidar"
+
+#def reward(state, action, info):
+#    speed = state[2].item()
+#    r = speed if speed < 10 else (10 - 5 * (speed - 10))
+#    return 0.1 * r
+
+env = IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+# %%
+model = PPO(
+    "MlpPolicy", env,
+    learning_rate=1e-4,
+    verbose=1,
+    tensorboard_log='runs/'
+)
+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_lidar_random.py

@@ -0,0 +1,49 @@
+# %%
+from stable_baselines3 import PPO
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+
+model_name = "ppo_speed_lidar_random"
+
+#def reward(state, action, info):
+#    speed = state[2].item()
+#    r = speed if speed < 10 else (10 - 5 * (speed - 10))
+#    return 0.1 * r
+
+env = IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+# %%
+model = PPO(
+    "MlpPolicy", env,
+    learning_rate=1e-4,
+    verbose=1,
+    tensorboard_log='runs/'
+)
+model.learn(total_timesteps=1000000)
+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/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/intersimple/trpo_speed_lidar.py

@@ -0,0 +1,52 @@
+# %%
+from sb3_contrib import TRPO
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+
+model_name = "trpo_speed_lidar"
+
+#def reward(state, action, info):
+#    speed = state[2].item()
+#    r = speed if speed < 10 else (10 - 5 * (speed - 10))
+#    return 0.1 * r
+
+env = IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+# %%
+model = TRPO(
+    "MlpPolicy", env,
+    learning_rate=1e-4,
+    verbose=1,
+    tensorboard_log='runs/',
+    #use_sde=True,
+    #sde_sample_freq=4,
+)
+model.learn(total_timesteps=1000000)
+model.save(model_name)
+
+print('Done training.')
+
+del model # remove to demonstrate saving and loading
+
+# %%
+model = TRPO.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/trpo/experiments/bc-intersimple-setobs2.py

@@ -0,0 +1,78 @@
+# %%
+import torch
+from core.policy import SetPolicy
+from tqdm import tqdm
+
+expert_data = torch.load('intersimple-expert-data-setobs2.pt')
+states, actions, _, dones = expert_data
+
+policy = SetPolicy(actions.shape[-1])
+
+policy = policy.cuda()
+optim = torch.optim.Adam(policy.parameters(), lr=1e-4)
+states = states[~dones].cuda()
+actions = actions[~dones].cuda()
+
+for _ in tqdm(range(10000)):
+    optim.zero_grad()
+    loss = -policy.log_prob(policy(states), actions).mean()
+    loss.backward()
+    optim.step()
+
+    print('Loss', loss)
+
+torch.save(policy.state_dict(), 'bc-intersimple-setobs2.pt')
+
+# %%
+import numpy as np
+from core.policy import SetPolicy
+from util.wrappers import Setobs, TransformObservation, CollisionPenaltyWrapper
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+
+policy = SetPolicy(actions.shape[-1])
+policy.load_state_dict(torch.load('bc-intersimple-setobs2.pt'))
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+env = Setobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+        n_rays=5,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+)
+
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action)
+    env.render(mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()
+
+# %%

scratch/etienne/trpo/experiments/gail-intersimple-minobs.py

@@ -0,0 +1,74 @@
+import gym
+from core.gail import gail, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Minobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Minobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(30)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3)
+
+expert_data = torch.load('intersimple-expert-data-minobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+)
+
+torch.save(policy.state_dict(), 'gail-intersimple-minobs.pt')

scratch/etienne/trpo/experiments/gail-intersimple-minobs2.py

@@ -0,0 +1,100 @@
+# %%
+import gym
+from core.gail import gail, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Minobs
+import numpy as np
+from gym.wrappers import TransformObservation
+from core.reparam_module import ReparamPolicy
+from torch.utils.tensorboard import SummaryWriter
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Minobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(50)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-5)
+
+expert_data = torch.load('intersimple-expert-data-minobs2.pt')
+expert_data = Buffer(*expert_data)
+
+# %%
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=500,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=800,
+    rollout_episodes=50,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    logger=SummaryWriter(comment='minobs2'),
+)
+
+torch.save(policy.state_dict(), 'gail-intersimple-minobs2.pt')
+
+# %%
+policy = Policy(env_fn(0).action_space.shape[0])
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('gail-intersimple-minobs2.pt'))
+
+env = env_fn(0)
+env.random_skip = False
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action)
+    env.render(mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()
+
+# %%

scratch/etienne/trpo/experiments/gail-intersimple-normobs.py

@@ -0,0 +1,74 @@
+import gym
+from core.gail import gail, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)) for _ in range(30)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-4)
+
+expert_data = torch.load('intersimple-expert-data-normobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=10,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+)
+
+torch.save(policy.state_dict(), 'gail-intersimple-normobs.pt')

scratch/etienne/trpo/experiments/gail-intersimple-setobs.py

@@ -0,0 +1,74 @@
+import gym
+from core.gail import gail, Buffer
+from core.value import SetValue
+from core.policy import SetPolicy
+from core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Setobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Setobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(30)]
+env_fn = lambda i: envs[i]
+
+policy = SetPolicy(env_fn(0).action_space.shape[0])
+
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3)
+
+expert_data = torch.load('intersimple-expert-data-setobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=500,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+)
+
+torch.save(policy.state_dict(), 'gail-intersimple-setobs.pt')

scratch/etienne/trpo/experiments/gail-intersimple-setobs2-recurrent.py

@@ -0,0 +1,97 @@
+# %%
+import gym
+from core.gail import gail, Buffer
+from core.value import SetValue
+from core.policy import SetPolicy
+from core.discriminator import RecurrentDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Setobs
+import numpy as np
+from gym.wrappers import TransformObservation
+from core.reparam_module import ReparamPolicy
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Setobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(50)]
+env_fn = lambda i: envs[i]
+
+policy = SetPolicy(env_fn(0).action_space.shape[0])
+
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+discriminator = RecurrentDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-5)
+
+expert_data = torch.load('intersimple-expert-data-setobs2.pt')
+expert_data = Buffer(*expert_data)
+
+# %%
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=500,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=800,
+    rollout_episodes=50,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+)
+
+torch.save(policy.state_dict(), 'gail-intersimple-setobs-recurrent.pt')
+
+# %%
+policy = SetPolicy(env_fn(0).action_space.shape[0])
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('gail-intersimple-setobs-recurrent.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action)
+    env.render(mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()
+
+# %%

scratch/etienne/trpo/experiments/gail-intersimple-setobs2.py

@@ -0,0 +1,101 @@
+# %%
+import gym
+from core.gail import gail, Buffer
+from core.value import SetValue
+from core.policy import SetPolicy
+from core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Setobs
+import numpy as np
+from gym.wrappers import TransformObservation
+from core.reparam_module import ReparamPolicy
+from torch.utils.tensorboard import SummaryWriter
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Setobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+    random_skip=True,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(50)]
+env_fn = lambda i: envs[i]
+
+policy = SetPolicy(env_fn(0).action_space.shape[0])
+
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-5)
+
+expert_data = torch.load('intersimple-expert-data-setobs2.pt')
+expert_data = Buffer(*expert_data)
+
+# %%
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=500,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=800,
+    rollout_episodes=50,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    logger=SummaryWriter(comment='setobs2-batchaug'),
+)
+
+torch.save(policy.state_dict(), 'gail-intersimple-setobs2.pt')
+
+# %%
+policy = SetPolicy(env_fn(0).action_space.shape[0])
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('gail-intersimple-setobs2.pt'))
+
+env = env_fn(0)
+env.random_skip = False
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action)
+    env.render(mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()
+
+# %%

scratch/etienne/trpo/experiments/gail-intersimple.py

@@ -0,0 +1,54 @@
+import gym
+from core.gail import gail, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper
+
+envs = [CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100) for _ in range(30)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-4)
+
+expert_data = torch.load('intersimple-expert-data.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=10,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+)
+
+torch.save(policy.state_dict(), 'gail-intersimple.pt')

scratch/etienne/trpo/experiments/gail-options-minobs.py

@@ -0,0 +1,97 @@
+import gym
+from options.options import gail
+from core.gail import Buffer
+from core.value import Value
+from core.policy import DiscretePolicy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, TransformObservation, Minobs
+import numpy as np
+from options.options import OptionsEnv
+from torch.utils.tensorboard import SummaryWriter
+from core.reparam_module import ReparamPolicy
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [OptionsEnv(Minobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+        n_rays=5,
+        agent=51,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (1, 5), (2, 5), (4, 5), (8, 5)]) for _ in range(60)]
+
+env_fn = lambda i: envs[i]
+policy = DiscretePolicy(env_fn(0).action_space.n)
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3)
+
+expert_data = torch.load('intersimple-expert-data-minobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=50,
+    rollout_episodes=60,
+    rollout_steps=60,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    logger=SummaryWriter(comment='-options-minobs'),
+)
+
+torch.save(policy.state_dict(), 'gail-options-minobs.pt')
+
+# %%
+policy = DiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('gail-options-minobs.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()

scratch/etienne/trpo/experiments/gail-options-setobs.py

@@ -0,0 +1,97 @@
+import gym
+from options.options import gail
+from core.gail import Buffer
+from core.value import SetValue
+from core.policy import SetDiscretePolicy
+from core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
+import numpy as np
+from options.options import OptionsEnv
+from torch.utils.tensorboard import SummaryWriter
+from core.reparam_module import ReparamPolicy
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [OptionsEnv(Setobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+        n_rays=5,
+        agent=51,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (1, 5), (2, 5), (4, 5), (8, 5)]) for _ in range(60)]
+
+env_fn = lambda i: envs[i]
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-3)
+
+expert_data = torch.load('intersimple-expert-data-setobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=150,
+    rollout_episodes=60,
+    rollout_steps=60,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    logger=SummaryWriter(comment='gail-options-setobs'),
+)
+
+torch.save(policy.state_dict(), 'gail-options-setobs.pt')
+
+# %%
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('gail-options-setobs.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()

scratch/etienne/trpo/experiments/gail-options-setobs2.py

@@ -0,0 +1,104 @@
+# %%
+import gym
+from options.options import gail
+from core.gail import Buffer
+from core.value import SetValue
+from core.policy import SetDiscretePolicy
+from core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
+import numpy as np
+from options.options import OptionsEnv
+from torch.utils.tensorboard import SummaryWriter
+from core.reparam_module import ReparamPolicy
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [OptionsEnv(Setobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+        n_rays=5,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5)]) for _ in range(60)]
+
+env_fn = lambda i: envs[i]
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-4)
+
+expert_data = torch.load('intersimple-expert-data-setobs2.pt')
+expert_data = Buffer(*expert_data)
+
+# %%
+def callback(epoch, value, policy):
+    if not epoch % 10:
+        torch.save(policy.state_dict(), f'gail-options-setobs2-{epoch}.pt')
+        torch.save(value.state_dict(), f'gail-options-setobs2-value-{epoch}.pt')
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=300,
+    rollout_episodes=60,
+    rollout_steps=60,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    logger=SummaryWriter(comment='gail-options-setobs2'),
+    callback=callback,
+)
+
+torch.save(policy.state_dict(), 'gail-options-setobs2.pt')
+
+# %%
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('gail-options-setobs2.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()

scratch/etienne/trpo/experiments/gail-pendulum.py

@@ -0,0 +1,39 @@
+import gym
+from core.gail import gail, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+
+env_fn = lambda _: gym.make('Pendulum-v0')
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3)
+
+expert_data = torch.load('trpo-pendulum-expert-data.pt')
+expert_data = Buffer(*expert_data)
+
+gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=10,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=100,
+    rollout_episodes=20,
+    rollout_steps=250,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+)
+
+
+torch.save(policy.state_dict(), 'gail-pendulum.pt')

scratch/etienne/trpo/experiments/gail-ppo-intersimple-minobs.py

@@ -0,0 +1,75 @@
+import gym
+from core.gail import gail_ppo, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Minobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Minobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(30)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3, weight_decay=1e-3)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3)
+
+expert_data = torch.load('intersimple-expert-data-minobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail_ppo(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+)
+
+torch.save(policy.state_dict(), 'gail-ppo-intersimple-minobs.pt')

scratch/etienne/trpo/experiments/gail-ppo-intersimple-normobs.py

@@ -0,0 +1,75 @@
+import gym
+from core.gail import gail_ppo, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)) for _ in range(30)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3)
+
+expert_data = torch.load('intersimple-expert-data-normobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail_ppo(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+)
+
+torch.save(policy.state_dict(), 'gail-ppo-intersimple-normobs.pt')

scratch/etienne/trpo/experiments/gail-ppo-intersimple-setobs2.py

@@ -0,0 +1,102 @@
+# %%
+import gym
+from core.gail import gail_ppo, Buffer
+from core.value import SetValue
+from core.policy import SetPolicy
+from core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Setobs
+import numpy as np
+from gym.wrappers import TransformObservation
+from core.reparam_module import ReparamPolicy
+from torch.utils.tensorboard import SummaryWriter
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Setobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+    random_skip=True,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(50)]
+env_fn = lambda i: envs[i]
+
+policy = SetPolicy(env_fn(0).action_space.shape[0])
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-5)
+
+expert_data = torch.load('intersimple-expert-data-setobs2.pt')
+expert_data = Buffer(*expert_data)
+
+# %%
+value, policy = gail_ppo(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=500,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=800,
+    rollout_episodes=50,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    logger=SummaryWriter(comment='-ppo-setobs2'),
+)
+
+torch.save(policy.state_dict(), 'gail-ppo-intersimple-setobs2.pt')
+
+# %%
+policy = SetPolicy(env_fn(0).action_space.shape[0])
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('gail-ppo-intersimple-setobs2.pt'))
+
+env = env_fn(0)
+env.random_skip = False
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action)
+    env.render(mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()
+
+# %%

scratch/etienne/trpo/experiments/gail-ppo-intersimple.py

@@ -0,0 +1,55 @@
+import gym
+from core.gail import gail_ppo, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper
+
+envs = [CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100) for _ in range(30)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=3e-4)
+
+expert_data = torch.load('intersimple-expert-data.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail_ppo(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=10,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+)
+
+torch.save(policy.state_dict(), 'gail-ppo-intersimple.pt')

scratch/etienne/trpo/experiments/gail-ppo-options-minobs.py

@@ -0,0 +1,96 @@
+import gym
+from options.options import gail_ppo, Buffer
+from core.value import Value
+from core.policy import DiscretePolicy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, TransformObservation, Minobs
+import numpy as np
+from options.options import OptionsEnv
+from torch.utils.tensorboard import SummaryWriter
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [OptionsEnv(Minobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+        n_rays=5,
+        agent=51,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (1, 5), (2, 5), (4, 5), (8, 5)]) for _ in range(60)]
+
+env_fn = lambda i: envs[i]
+
+policy = DiscretePolicy(env_fn(0).action_space.n)
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3)
+
+expert_data = torch.load('intersimple-expert-data-minobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail_ppo(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=50,
+    rollout_episodes=60,
+    rollout_steps=60,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    logger=SummaryWriter(comment='gail-ppo-options-minobs'),
+)
+
+torch.save(policy.state_dict(), 'gail-ppo-options-minobs.pt')
+
+# %%
+policy = DiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy.load_state_dict(torch.load('gail-ppo-options-minobs.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()

scratch/etienne/trpo/experiments/gail-ppo-options-setobs.py

@@ -0,0 +1,96 @@
+import gym
+from options.options import gail_ppo, Buffer
+from core.value import SetValue
+from core.policy import SetDiscretePolicy
+from core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
+import numpy as np
+from options.options import OptionsEnv
+from torch.utils.tensorboard import SummaryWriter
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [OptionsEnv(Setobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+        n_rays=5,
+        agent=51,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (1, 5), (2, 5), (4, 5), (8, 5)]) for _ in range(60)]
+
+env_fn = lambda i: envs[i]
+
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-3)
+
+expert_data = torch.load('intersimple-expert-data-setobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail_ppo(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=150,
+    rollout_episodes=60,
+    rollout_steps=60,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    logger=SummaryWriter(comment='gail-ppo-options-setobs'),
+)
+
+torch.save(policy.state_dict(), 'gail-ppo-options-setobs.pt')
+
+# %%
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy.load_state_dict(torch.load('gail-ppo-options-setobs.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()

scratch/etienne/trpo/experiments/gail-ppo-options-setobs2.py

@@ -0,0 +1,103 @@
+# %%
+import gym
+from options.options import gail_ppo, Buffer
+from core.value import SetValue
+from core.policy import SetDiscretePolicy
+from core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
+import numpy as np
+from options.options import OptionsEnv
+from torch.utils.tensorboard import SummaryWriter
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [OptionsEnv(Setobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+        n_rays=5,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5)]) for _ in range(60)]
+
+env_fn = lambda i: envs[i]
+
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-4)
+
+expert_data = torch.load('intersimple-expert-data-setobs2.pt')
+expert_data = Buffer(*expert_data)
+
+# %%
+def callback(epoch, value, policy):
+    if not epoch % 10:
+        torch.save(policy.state_dict(), f'gail-ppo-options-setobs2-{epoch}.pt')
+        torch.save(value.state_dict(), f'gail-ppo-options-setobs2-value-{epoch}.pt')
+
+value, policy = gail_ppo(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=200,
+    rollout_episodes=60,
+    rollout_steps=60,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    logger=SummaryWriter(comment='gail-ppo-options-setobs2'),
+    callback=callback,
+)
+
+torch.save(policy.state_dict(), 'gail-ppo-options-setobs2.pt')
+
+# %%
+policy = SetDiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy.load_state_dict(torch.load('gail-ppo-options-setobs2.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()

scratch/etienne/trpo/experiments/intersimple-expert-rollout-minobs.py

@@ -0,0 +1,54 @@
+import torch
+import functools
+from core.sampling import rollout_sb3
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+from intersim.expert import NormalizedIntersimpleExpert
+from util.wrappers import CollisionPenaltyWrapper, Minobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+env = IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+policy = NormalizedIntersimpleExpert(env, mu=0.001)
+
+env = Minobs(TransformObservation(
+    CollisionPenaltyWrapper(
+        env,
+        collision_distance=6, collision_penalty=100
+    ), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
+))
+expert_data = rollout_sb3(env, policy, n_episodes=64, max_steps_per_episode=200)
+
+states, actions, rewards, dones = expert_data
+print(f'Expert mean episode length {(~dones).sum() / states.shape[0]}')
+print(f'Expert mean reward per episode {rewards[~dones].sum() / states.shape[0]}')
+print(f'Observation mean', states[~dones].mean(0))
+print(f'Observation std', states[~dones].std(0))
+
+torch.save(expert_data, 'intersimple-expert-data-minobs.pt')

scratch/etienne/trpo/experiments/intersimple-expert-rollout-minobs2.py

@@ -0,0 +1,53 @@
+import torch
+import functools
+from core.sampling import rollout_sb3
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+from intersim.expert import NormalizedIntersimpleExpert
+from util.wrappers import CollisionPenaltyWrapper, Minobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+env = IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+policy = NormalizedIntersimpleExpert(env, mu=0.001)
+
+env = Minobs(TransformObservation(
+    CollisionPenaltyWrapper(
+        env,
+        collision_distance=6, collision_penalty=100
+    ), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
+))
+expert_data = rollout_sb3(env, policy, n_episodes=2048, max_steps_per_episode=200)
+
+states, actions, rewards, dones = expert_data
+print(f'Expert mean episode length {(~dones).sum() / states.shape[0]}')
+print(f'Expert mean reward per episode {rewards[~dones].sum() / states.shape[0]}')
+print(f'Observation mean', states[~dones].mean(0))
+print(f'Observation std', states[~dones].std(0))
+
+torch.save(expert_data, 'intersimple-expert-data-minobs2.pt')

scratch/etienne/trpo/experiments/intersimple-expert-rollout-normobs.py

@@ -0,0 +1,54 @@
+import torch
+import functools
+from core.sampling import rollout_sb3
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+from intersim.expert import NormalizedIntersimpleExpert
+from util.wrappers import CollisionPenaltyWrapper
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+env = IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+policy = NormalizedIntersimpleExpert(env, mu=0.001)
+
+env = TransformObservation(
+    CollisionPenaltyWrapper(
+        env,
+        collision_distance=6, collision_penalty=100
+    ), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
+)
+expert_data = rollout_sb3(env, policy, n_episodes=64, max_steps_per_episode=200)
+
+states, actions, rewards, dones = expert_data
+print(f'Expert mean episode length {(~dones).sum() / states.shape[0]}')
+print(f'Expert mean reward per episode {rewards[~dones].sum() / states.shape[0]}')
+print(f'Observation mean', states[~dones].mean(0))
+print(f'Observation std', states[~dones].std(0))
+
+torch.save(expert_data, 'intersimple-expert-data-normobs.pt')

scratch/etienne/trpo/experiments/intersimple-expert-rollout-setobs.py

@@ -0,0 +1,54 @@
+import torch
+import functools
+from core.sampling import rollout_sb3
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+from intersim.expert import NormalizedIntersimpleExpert
+from util.wrappers import CollisionPenaltyWrapper, Setobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+env = IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+policy = NormalizedIntersimpleExpert(env, mu=0.001)
+
+env = Setobs(TransformObservation(
+    CollisionPenaltyWrapper(
+        env,
+        collision_distance=6, collision_penalty=100
+    ), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
+))
+expert_data = rollout_sb3(env, policy, n_episodes=64, max_steps_per_episode=200)
+
+states, actions, rewards, dones = expert_data
+print(f'Expert mean episode length {(~dones).sum() / states.shape[0]}')
+print(f'Expert mean reward per episode {rewards[~dones].sum() / states.shape[0]}')
+print(f'Observation mean', states[~dones].mean(0))
+print(f'Observation std', states[~dones].std(0))
+
+torch.save(expert_data, 'intersimple-expert-data-setobs.pt')

scratch/etienne/trpo/experiments/intersimple-expert-rollout-setobs2.py

@@ -0,0 +1,53 @@
+import torch
+import functools
+from core.sampling import rollout_sb3
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+from intersim.expert import NormalizedIntersimpleExpert
+from util.wrappers import CollisionPenaltyWrapper, Setobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+env = IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+)
+
+policy = NormalizedIntersimpleExpert(env, mu=0.001)
+
+env = Setobs(TransformObservation(
+    CollisionPenaltyWrapper(
+        env,
+        collision_distance=6, collision_penalty=100
+    ), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
+))
+expert_data = rollout_sb3(env, policy, n_episodes=2048, max_steps_per_episode=200)
+
+states, actions, rewards, dones = expert_data
+print(f'Expert mean episode length {(~dones).sum() / states.shape[0]}')
+print(f'Expert mean reward per episode {rewards[~dones].sum() / states.shape[0]}')
+print(f'Observation mean', states[~dones].mean(0))
+print(f'Observation std', states[~dones].std(0))
+
+torch.save(expert_data, 'intersimple-expert-data-setobs2.pt')

scratch/etienne/trpo/experiments/intersimple-expert-rollout.py

@@ -0,0 +1,25 @@
+import torch
+import functools
+from core.sampling import rollout_sb3
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+from intersim.expert import NormalizedIntersimpleExpert
+from util.wrappers import CollisionPenaltyWrapper
+
+env = CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+), collision_distance=6, collision_penalty=100)
+policy = NormalizedIntersimpleExpert(env.env, mu=0.001)
+
+expert_data = rollout_sb3(env, policy, n_episodes=64, max_steps_per_episode=200)
+
+states, actions, rewards, dones = expert_data
+print(f'Expert mean episode length {(~dones).sum() / states.shape[0]}')
+print(f'Expert mean reward per episode {rewards[~dones].sum() / states.shape[0]}')
+
+torch.save(expert_data, 'intersimple-expert-data.pt')

scratch/etienne/trpo/experiments/ppo-intersimple-minobs.py

@@ -0,0 +1,63 @@
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+
+from core.ppo import ppo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+import numpy as np
+from gym.wrappers import TransformObservation
+
+from util.wrappers import Minobs
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Minobs(TransformObservation(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(30)]
+
+env_fn = lambda i: envs[i]
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+value, policy = ppo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=50,
+    rollout_episodes=30,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    v_opt=v_opt,
+    v_iters=1000,
+)
+
+torch.save(policy.state_dict(), 'ppo-intersimple.pt')

scratch/etienne/trpo/experiments/ppo-intersimple-minobs2.py

@@ -0,0 +1,62 @@
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+
+from core.ppo import ppo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+import numpy as np
+from gym.wrappers import TransformObservation
+
+from util.wrappers import Minobs
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Minobs(TransformObservation(IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=1000
+    ),
+), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(30)]
+
+env_fn = lambda i: envs[i]
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3, weight_decay=1e-3)
+
+value, policy = ppo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=50,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    v_opt=v_opt,
+    v_iters=1000,
+)
+
+torch.save(policy.state_dict(), 'ppo-intersimple.pt')

scratch/etienne/trpo/experiments/ppo-intersimple-normobs.py

@@ -0,0 +1,61 @@
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+
+from core.ppo import ppo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [TransformObservation(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=10
+    ),
+), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)) for _ in range(30)]
+
+env_fn = lambda i: envs[i]
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+value, policy = ppo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=50,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    v_opt=v_opt,
+    v_iters=1000,
+)
+
+torch.save(policy.state_dict(), 'ppo-intersimple.pt')

scratch/etienne/trpo/experiments/ppo-intersimple.py

@@ -0,0 +1,41 @@
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+
+from core.ppo import ppo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+
+envs = [IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=10
+    ),
+) for _ in range(30)]
+
+env_fn = lambda i: envs[i]
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+value, policy = ppo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=50,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    v_opt=v_opt,
+    v_iters=1000,
+)
+
+torch.save(policy.state_dict(), 'ppo-intersimple.pt')

scratch/etienne/trpo/experiments/ppo-options-minobs.py

@@ -0,0 +1,89 @@
+# %%
+import gym
+from core.sampling import rollout
+from core.ppo import ppo
+from core.value import Value
+from core.policy import DiscretePolicy
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+import numpy as np
+from util.wrappers import CollisionPenaltyWrapper, TransformObservation
+
+from util.wrappers import Minobs
+from options.options import OptionsEnv
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [OptionsEnv(Minobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+        n_rays=5,
+        agent=51,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (5, 5), (10, 5)]) for _ in range(50)]
+
+env_fn = lambda i: envs[i]
+policy = DiscretePolicy(env_fn(0).action_space.n)
+value = Value()
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+# %%
+value, policy = ppo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=50,
+    rollout_episodes=30,
+    rollout_steps=20,
+    gamma=0.99,
+    gae_lambda=0.95,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    v_opt=v_opt,
+    v_iters=1000,
+)
+
+torch.save(policy.state_dict(), 'ppo-options-minobs.pt')
+
+# %%
+policy = DiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy.load_state_dict(torch.load('ppo-options-minobs.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()
+
+# %%

scratch/etienne/trpo/experiments/ppo-pendulum.py

@@ -0,0 +1,27 @@
+import gym
+from core.ppo import ppo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+
+env_fn = lambda _: gym.make('Pendulum-v0')
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+ppo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=300,
+    rollout_episodes=100,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    v_opt=v_opt,
+    v_iters=1000,
+)

scratch/etienne/trpo/experiments/readme.md

@@ -0,0 +1,7 @@
+|                     | TRPO | PPO   | GAIL  | GAIL PPO | WGAIL | WGAIL PPO |
+|---------------------|------|-------|-------|----------|-------|-----------|
+| Pendulum            | -120 | -1000 | -120  | -1000    | -120  | -1000     |
+| intersimple-minobs  | +1@30|       | +6@26 | +1@20    | -7000@26, -2000@60 | -6000@30, -5000@60 |
+| intersimple-setobs  |      |       | -200@20 |        |       |           |
+| intersimple-minobs2 |      |       | -1500@800 |      |       |           |
+| intersimple-setobs2 |      |       | -500@800 | -750@800 | -1300@800 | -2500@600, unstable |

scratch/etienne/trpo/experiments/requirements.txt

@@ -0,0 +1,3 @@
+torch
+stable-baselines3
+gym

scratch/etienne/trpo/experiments/sgail-options-setobs2.py

@@ -0,0 +1,111 @@
+# %%
+import sys
+sys.path.append('../../../../')
+
+import gym
+from src.safe_options.options import gail
+from src.core.gail import Buffer
+from src.core.value import SetValue
+from src.safe_options.policy import SetMaskedDiscretePolicy
+from src.core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from src.util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
+import numpy as np
+from src.safe_options.options import SafeOptionsEnv
+from torch.utils.tensorboard import SummaryWriter
+from src.core.reparam_module import ReparamPolicy
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [SafeOptionsEnv(Setobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+        n_rays=5,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=True,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (10, 5)], safe_actions_collision_method='circle', abort_unsafe_collision_method='circle') for _ in range(60)]
+
+env_fn = lambda i: envs[i]
+policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n)
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-4)
+
+expert_data = torch.load('intersimple-expert-data-setobs2.pt')
+expert_data = Buffer(*expert_data)
+
+# %%
+def callback(epoch, value, policy):
+    if not epoch % 10:
+        torch.save(policy.state_dict(), f'sgail-options-setobs2-{epoch}.pt')
+        torch.save(value.state_dict(), f'sgail-options-setobs2-value-{epoch}.pt')
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=300,
+    rollout_episodes=60,
+    rollout_steps=60,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    logger=SummaryWriter(comment='sgail-options-setobs2'),
+    callback=callback,
+)
+
+torch.save(policy.state_dict(), 'sgail-options-setobs2.pt')
+
+# %%
+policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space['observation'].shape), torch.zeros(env_fn(0).observation_space['safe_actions'].shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('sgail-options-setobs2.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    action = policy.sample(policy(
+        torch.tensor(obs['observation'], dtype=torch.float32),
+        torch.tensor(obs['safe_actions'], dtype=torch.float32),
+    ))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()
+
+# %%

scratch/etienne/trpo/experiments/sgail-ppo-options-setobs2.py

@@ -0,0 +1,110 @@
+# %%
+import sys
+sys.path.append('../../../../')
+
+import gym
+from src.safe_options.options import gail_ppo, Buffer
+from src.core.value import SetValue
+from src.safe_options.policy import SetMaskedDiscretePolicy
+from src.core.discriminator import DeepsetDiscriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from src.util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
+import numpy as np
+from src.safe_options.options import SafeOptionsEnv
+from torch.utils.tensorboard import SummaryWriter
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [SafeOptionsEnv(Setobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+        n_rays=5,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=True,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (10, 5)], safe_actions_collision_method='circle', abort_unsafe_collision_method='circle') for _ in range(60)]
+
+env_fn = lambda i: envs[i]
+
+policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n)
+pi_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3)
+
+discriminator = DeepsetDiscriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-4)
+
+expert_data = torch.load('intersimple-expert-data-setobs2.pt')
+expert_data = Buffer(*expert_data)
+
+# %%
+def callback(epoch, value, policy):
+    if not epoch % 10:
+        torch.save(policy.state_dict(), f'sgail-ppo-options-setobs2-{epoch}.pt')
+        torch.save(value.state_dict(), f'sgail-ppo-options-setobs2-value-{epoch}.pt')
+
+value, policy = gail_ppo(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=200,
+    rollout_episodes=60,
+    rollout_steps=60,
+    gamma=0.99,
+    gae_lambda=0.9,
+    clip_ratio=0.2,
+    pi_opt=pi_opt,
+    pi_iters=100,
+    logger=SummaryWriter(comment='sgail-ppo-options-setobs2'),
+    callback=callback,
+)
+
+torch.save(policy.state_dict(), 'sgail-ppo-options-setobs2.pt')
+
+# %%
+policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space['observation'].shape), torch.zeros(env_fn(0).observation_space['safe_actions'].shape))
+policy.load_state_dict(torch.load('sgail-ppo-options-setobs2.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    action = policy.sample(policy(
+        torch.tensor(obs['observation'], dtype=torch.float32),
+        torch.tensor(obs['safe_actions'], dtype=torch.float32),
+    ))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward, 'safe actions', obs['safe_actions'])
+    if done:
+        break
+env.close()
+# %%

scratch/etienne/trpo/experiments/trpo-intersimple-minobs.py

@@ -0,0 +1,88 @@
+# %%
+import gym
+from core.sampling import rollout
+from core.trpo import trpo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+import numpy as np
+from gym.wrappers import TransformObservation
+from util.wrappers import CollisionPenaltyWrapper
+from core.reparam_module import ReparamPolicy
+
+from util.wrappers import Minobs
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Minobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(50)]
+
+env_fn = lambda i: envs[i]
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+# %%
+value, policy = trpo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=50,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.95,
+    delta=0.01,
+    backtrack_coeff=0.9,
+    backtrack_iters=50,
+    v_opt=v_opt,
+    v_iters=1000,
+    cg_damping=0.1,
+)
+
+torch.save(policy.state_dict(), 'trpo-intersimple-minobs.pt')
+
+# %%
+policy = Policy(env_fn(0).action_space.shape[0])
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('trpo-intersimple-minobs.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action)
+    env.render(mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()

scratch/etienne/trpo/experiments/trpo-intersimple-minobs2.py

@@ -0,0 +1,87 @@
+# %%
+import gym
+from core.sampling import rollout
+from core.trpo import trpo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+import numpy as np
+from gym.wrappers import TransformObservation
+from util.wrappers import CollisionPenaltyWrapper
+from core.reparam_module import ReparamPolicy
+
+from util.wrappers import Minobs
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Minobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(50)]
+
+env_fn = lambda i: envs[i]
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+# %%
+value, policy = trpo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=200,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.95,
+    delta=0.01,
+    backtrack_coeff=0.9,
+    backtrack_iters=50,
+    v_opt=v_opt,
+    v_iters=1000,
+    cg_damping=0.1,
+)
+
+torch.save(policy.state_dict(), 'trpo-intersimple-minobs2.pt')
+
+# %%
+policy = Policy(env_fn(0).action_space.shape[0])
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('trpo-intersimple-minobs2.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action)
+    env.render(mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()

scratch/etienne/trpo/experiments/trpo-intersimple-normobs.py

@@ -0,0 +1,62 @@
+import gym
+from core.sampling import rollout
+from core.trpo import trpo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [TransformObservation(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=10
+    ),
+), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)) for _ in range(50)]
+
+env_fn = lambda i: envs[i]
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+value, policy = trpo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.95,
+    delta=0.01,
+    backtrack_coeff=0.9,
+    backtrack_iters=50,
+    v_opt=v_opt,
+    v_iters=1000,
+    cg_damping=0.1,
+)
+
+#rollout(env_fn, policy, n_episodes=9, max_steps_per_episode=200, render=True)

scratch/etienne/trpo/experiments/trpo-intersimple-setobs.py

@@ -0,0 +1,90 @@
+# %%
+import gym
+from core.sampling import rollout
+from core.trpo import trpo
+from core.value import SetValue
+from core.policy import DeepSetPolicy
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+import numpy as np
+from gym.wrappers import TransformObservation
+from util.wrappers import CollisionPenaltyWrapper
+from core.reparam_module import ReparamPolicy
+
+from util.wrappers import Setobs
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Setobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(50)]
+
+env_fn = lambda i: envs[i]
+policy = DeepSetPolicy(env_fn(0).action_space.shape[0])
+value = SetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+# %%
+value, policy = trpo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=150,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.95,
+    delta=0.01,
+    backtrack_coeff=0.9,
+    backtrack_iters=50,
+    v_opt=v_opt,
+    v_iters=1000,
+    cg_damping=0.1,
+)
+
+torch.save(policy.state_dict(), 'trpo-intersimple-setobs.pt')
+
+# %%
+policy = DeepSetPolicy(env_fn(0).action_space.shape[0])
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('trpo-intersimple-setobs.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action)
+    env.render(mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()
+
+# %%

scratch/etienne/trpo/experiments/trpo-intersimple-setobs2.py

@@ -0,0 +1,87 @@
+# %%
+import gym
+from core.sampling import rollout
+from core.trpo import trpo
+from core.value import DeepSetValue
+from core.policy import DeepSetPolicy
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+import numpy as np
+from gym.wrappers import TransformObservation
+from util.wrappers import CollisionPenaltyWrapper
+from core.reparam_module import ReparamPolicy
+
+from util.wrappers import Setobs
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Setobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(50)]
+
+env_fn = lambda i: envs[i]
+policy = DeepSetPolicy(env_fn(0).action_space.shape[0])
+value = DeepSetValue()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+# %%
+value, policy = trpo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=200,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.95,
+    delta=0.01,
+    backtrack_coeff=0.9,
+    backtrack_iters=50,
+    v_opt=v_opt,
+    v_iters=1000,
+    cg_damping=0.1,
+)
+
+torch.save(policy.state_dict(), 'trpo-intersimple-setobs2.pt')
+
+# %%
+policy = DeepSetPolicy(env_fn(0).action_space.shape[0])
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('trpo-intersimple-setobs2.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action)
+    env.render(mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()

scratch/etienne/trpo/experiments/trpo-intersimple.py

@@ -0,0 +1,42 @@
+import gym
+from core.sampling import rollout
+from core.trpo import trpo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+
+envs = [IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=10
+    ),
+) for _ in range(50)]
+
+env_fn = lambda i: envs[i]
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+value, policy = trpo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=100,
+    gamma=0.99,
+    gae_lambda=0.95,
+    delta=0.01,
+    backtrack_coeff=0.9,
+    backtrack_iters=50,
+    v_opt=v_opt,
+    v_iters=1000,
+    cg_damping=0.1,
+)
+
+#rollout(env_fn, policy, n_episodes=9, max_steps_per_episode=200, render=True)

scratch/etienne/trpo/experiments/trpo-options-minobs.py

@@ -0,0 +1,91 @@
+# %%
+import gym
+from core.sampling import rollout
+from core.trpo import trpo
+from core.value import Value
+from core.policy import DiscretePolicy
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+import numpy as np
+from util.wrappers import CollisionPenaltyWrapper, TransformObservation
+from core.reparam_module import ReparamPolicy
+
+from util.wrappers import Minobs
+from options.options import OptionsEnv
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [OptionsEnv(Minobs(
+    TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+        n_rays=5,
+        agent=51,
+        reward=functools.partial(
+            speed_reward,
+            collision_penalty=0
+        ),
+        stop_on_collision=False,
+    ), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
+), options=[(0, 5), (5, 5), (10, 5)]) for _ in range(50)]
+
+env_fn = lambda i: envs[i]
+policy = DiscretePolicy(env_fn(0).action_space.n)
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+# %%
+value, policy = trpo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=50,
+    rollout_episodes=30,
+    rollout_steps=20,
+    gamma=0.99,
+    gae_lambda=0.95,
+    delta=0.01,
+    backtrack_coeff=0.9,
+    backtrack_iters=50,
+    v_opt=v_opt,
+    v_iters=1000,
+    cg_damping=0.1,
+)
+
+torch.save(policy.state_dict(), 'trpo-options-minobs.pt')
+
+# %%
+policy = DiscretePolicy(env_fn(0).action_space.n)
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('trpo-options-minobs.pt'))
+
+env = env_fn(0)
+obs = env.reset()
+env.render(mode='post')
+for i in range(300):
+    #action, _ = policy.predict(torch.tensor(obs))
+    action = policy.sample(policy(torch.tensor(obs, dtype=torch.float32)))
+    obs, reward, done, _ = env.step(action, render_mode='post')
+    print('step', i, 'reward', reward)
+    if done:
+        break
+env.close()
+
+# %%

scratch/etienne/trpo/experiments/trpo-pendulum-rollout.py

@@ -0,0 +1,17 @@
+import gym
+from core.gail import gail
+from core.reparam_module import ReparamPolicy
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from core.sampling import rollout
+
+env_fn = lambda _: gym.make('Pendulum-v0')
+policy = Policy(env_fn(0).action_space.shape[0])
+policy(torch.zeros(env_fn(0).observation_space.shape))
+policy = ReparamPolicy(policy)
+policy.load_state_dict(torch.load('trpo-pendulum.pt'))
+
+expert_data = rollout(env_fn, policy, n_episodes=20, max_steps_per_episode=200)
+torch.save(expert_data, 'trpo-pendulum-expert-data.pt')

scratch/etienne/trpo/experiments/trpo-pendulum.py

@@ -0,0 +1,30 @@
+import gym
+from gym.wrappers import TransformObservation
+from core.trpo import trpo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+
+env_fn = lambda _: TransformObservation(gym.make('Pendulum-v0'), lambda obs: obs)
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-3, weight_decay=1e-4)
+
+value, policy = trpo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=100,
+    rollout_episodes=20,
+    rollout_steps=250,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    v_opt=v_opt,
+    v_iters=1000,
+)
+
+
+torch.save(policy.state_dict(), 'trpo-pendulum.pt')

scratch/etienne/trpo/experiments/trpo-walker.py

@@ -0,0 +1,26 @@
+import gym
+from core.trpo import trpo
+from core.value import Value
+from core.policy import Policy
+import torch.optim
+
+env_fn = lambda _: gym.make('BipedalWalker-v3')
+policy = Policy(env_fn(0).action_space.shape[0])
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-2)
+
+trpo(
+    env_fn=env_fn,
+    value=value,
+    policy=policy,
+    epochs=1000,
+    rollout_episodes=20,
+    rollout_steps=250,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    v_opt=v_opt,
+    v_iters=1000,
+)

scratch/etienne/trpo/experiments/vec-env.ipynb

@@ -0,0 +1,346 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stable_baselines3.common.env_util import make_vec_env\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "env = make_vec_env('Pendulum-v0', n_envs=6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6, 3)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obs = env.reset()\n",
+    "obs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
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+     ]
+    }
+   ],
+   "source": [
+    "dones = [False]\n",
+    "i = 0\n",
+    "while not any(dones):\n",
+    "    i += 1\n",
+    "    print(i)\n",
+    "    _, _, dones, _ = env.step(np.zeros((6, 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True,  True,  True,  True,  True])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, _, dones, _ = env.step(np.zeros((6, 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False, False, False])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "6c7a4ac80dd345f83235e10baa3acc437d966916e1cc075a45b91bb9cc030938"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.7 64-bit ('.venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

scratch/etienne/trpo/experiments/wgail-intersimple-minobs.py

@@ -0,0 +1,76 @@
+import gym
+from core.gail import gail, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlat
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Minobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Minobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlat(
+    n_rays=5,
+    agent=51,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(30)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.Adam(discriminator.parameters(), lr=1e-3, weight_decay=1e-2)
+
+expert_data = torch.load('intersimple-expert-data-minobs.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=100,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=4000,
+    rollout_episodes=30,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    wasserstein=True,
+    wasserstein_c=1.,
+)
+
+torch.save(policy.state_dict(), 'wgail-intersimple-minobs.pt')

scratch/etienne/trpo/experiments/wgail-intersimple-minobs2.py

@@ -0,0 +1,75 @@
+import gym
+from core.gail import gail, Buffer
+from core.value import Value
+from core.policy import Policy
+from core.discriminator import Discriminator
+import torch.optim
+from intersim.envs import IntersimpleLidarFlatRandom
+from intersim.envs.intersimple import speed_reward
+import functools
+from util.wrappers import CollisionPenaltyWrapper, Minobs
+import numpy as np
+from gym.wrappers import TransformObservation
+
+obs_min = np.array([
+    [-1000, -1000, 0, -np.pi, -1e-1, 0.],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+    [0, -np.pi, -20, -20, -np.pi, -1e-1],
+]).reshape(-1)
+
+obs_max = np.array([
+    [1000, 1000, 20, np.pi, 1e-1, 0.],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+    [50, np.pi, 20, 20, np.pi, 1e-1],
+]).reshape(-1)
+
+envs = [Minobs(TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
+    n_rays=5,
+    reward=functools.partial(
+        speed_reward,
+        collision_penalty=0
+    ),
+    stop_on_collision=False,
+), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))) for _ in range(50)]
+env_fn = lambda i: envs[i]
+
+policy = Policy(env_fn(0).action_space.shape[0])
+
+value = Value()
+v_opt = torch.optim.Adam(value.parameters(), lr=1e-4, weight_decay=1e-3)
+
+discriminator = Discriminator()
+disc_opt = torch.optim.RMSprop(discriminator.parameters(), lr=1e-3, weight_decay=1e-5)
+
+expert_data = torch.load('intersimple-expert-data-minobs2.pt')
+expert_data = Buffer(*expert_data)
+
+value, policy = gail(
+    env_fn=env_fn,
+    expert_data=expert_data,
+    discriminator=discriminator,
+    disc_opt=disc_opt,
+    disc_iters=500,
+    policy=policy,
+    value=value,
+    v_opt=v_opt,
+    v_iters=1000,
+    epochs=4000,
+    rollout_episodes=50,
+    rollout_steps=200,
+    gamma=0.99,
+    gae_lambda=0.9,
+    delta=0.01,
+    backtrack_coeff=0.8,
+    backtrack_iters=10,
+    wasserstein=True,
+    wasserstein_c=0.1,
+)
+
+torch.save(policy.state_dict(), 'wgail-intersimple-minobs2.pt')