Add flag to skip seeds

README.md

@@ -19,53 +19,28 @@ The INTERACTION dataset contains a two folders which should be copied into a fol
   - the contents of `recorded_trackfiles` should be copied to `./InteractionSimulator/datasets/trackfiles`
   - the contents of `maps` should be copied to `./InteractionSimulator/datasets/maps`
 
-## Processing, saving, and loading expert demos
+## Processing and saving expert demos
-Once the repository has been set up, you can process and save expert track demonstrations with:
+Once the repository has been set up, you need to generate two separate sets of expert demos for tracks 0-4. The first command generates true joint and individual states and actions necessary for evaluating, saving them in `expert_data/`. The second command generates trajectory rollouts according to individual agent observations, which is later used as expert data for the learning models.
 ```
-python src/expert_data.py --loc [LOCNUM] --track [TRACKNUM]
+python -m src.expert --locs='[DR_USA_Roundabout_FT]' --tracks='[0,1,2,3,4]'
-```
+python -m intersimple-expert-rollout-setobs2 --tracks='[0,1,2,3,4]'
-You can (and should) process all tracks at once at location 0 with:
-```
-python src/expert_data.py --all-tracks
-```
-
-You can then train a default behavior cloning policy with the following. Be sure to check help for main.py for running options.
-```
-python src/main.py --train
-```
-You can run tensorboard by running the following and opening `localhost:6006` (or alternatively port-forwarding 6006 from the remote server)
-```
-tensorboard --logdir output/
-```
-You can then test the learned policy with the following, and see the animation file in `output/`:
-```
-python src/main.py --test
 ```
 
 
-You can load the experts actions manually
+## Tuning hyperparameters and training finalized models
-```
+To tune models, we use `ray[tune]` grid searches. You can run see the commands we used to train in the top half of `train_models.sh`, as well as the hyperparameters we search over in `bc-experiment.py`, `gail-experiment.py`, and `shail-experiment.py`. After training the models, configurations get saved in `best_configs/` (the best SHAIL confg gets copied to a HAIL config, with the appropriate environment parameters changed for ablation). However, upon manual inspection of the training runs, we note some better performance than the automatically-set configs at earlier epochs, so we adjust the `best_configs` manually.
-from src import expert_data
+
-observations, actions = expert_data.load_expert_data(loc = [LOCNUM], track = [TRACKNUM])
+After the `best_configs/` are set, we rerun each configuration with multiple seeds. The commands to do so are in the bottom half of `train_models.sh`. This saves different learned policy files to `test_policies/`.
-for (s, a) in zip (observations, actions):
+
-    # do some imitation learning
+
-```
+## Evaluating models
+To evaluate the learned policies, we rerun each model in particular setting, evaluate all our metrics, and average over different trained model seeds. The commands to do so are in `evaluate_models.sh`. 
 
 
 ## Package Structure
 ```
 InteractionImitation
-|- demos
+|- TODO
-|- algorithms
-   |- BC
-   |- AdVIL
-|- nets
-   |- Encoder
-   |- DeepSet
-   |- Decoder
-|- policies
-|- discriminators
-|- demo_generators
 ```
 
 ## Type Definitions

bc-experiment.py

@@ -142,7 +142,7 @@ if __name__ == '__main__':
     import argparse
     parser = argparse.ArgumentParser()
     parser.add_argument('--train', choices=['A', 'B'])
-    parser.add_argument('--epochs', type=int, default=1000)
+    parser.add_argument('--epochs', type=int, default=500)
     parser.add_argument('--test', type=str, help='path to config file to run final training on')
     parser.add_argument('--test_seeds', type=int, default=5)
     parser.add_argument('--test_cpus', type=int, help='number of cpus available to split test seed training over')
@@ -162,10 +162,10 @@ if __name__ == '__main__':
                 },
                 'policy': {
                     'learning_rate': 3e-4, 
-                    'learning_rate_decay': 1.0, 
+                    'learning_rate_decay': tune.grid_search([0.999, 1.0]), 
-                    'hidden_layer_size': tune.grid_search([20, 40]),
+                    'hidden_layer_size': tune.grid_search([10, 20, 40]),
                     'n_hidden_layers': tune.grid_search([2, 3]), 
-                    'activation':0,
+                    'activation':tune.grid_search([0, 1]),
                 },
                 'train_epochs': args.epochs,
                 'seed': 0,
@@ -210,3 +210,5 @@ if __name__ == '__main__':
             check_dir = analysis._checkpoints[i]['logdir']
             shutil.copyfile(os.path.join(check_dir,'policy_final.pt'), 
                 os.path.join(savepath, f'policy_seed{s}.pt'))
+            shutil.copyfile(os.path.join(check_dir,'params.json'),
+                os.path.join(savepath, 'config.json')) # copy config automatically

best_configs/hail-etienne2_expA.json

@@ -1,33 +0,0 @@
-{
-    "experiment": "A",
-    "trainenv": {
-        "stop_on_collision": false,
-        "safe_actions_collision_method": null,
-        "abort_unsafe_collision_method": null
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "clip_ratio": 0.2,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 10,
-        "n_hidden_layers": 3,
-        "activation": 0,
-        "option": 0
-    },
-    "value": {
-        "learning_rate": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 3,
-        "n_hidden_layers_global": 2,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

best_configs/hail-etienne2_expB.json

@@ -1,33 +0,0 @@
-{
-    "experiment": "B",
-    "trainenv": {
-        "stop_on_collision": false,
-        "safe_actions_collision_method": null,
-        "abort_unsafe_collision_method": null
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "clip_ratio": 0.2,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 10,
-        "n_hidden_layers": 3,
-        "activation": 0,
-        "option": 0
-    },
-    "value": {
-        "learning_rate": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 3,
-        "n_hidden_layers_global": 2,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

best_configs/hail-etienne3_expB.json

@@ -1,33 +0,0 @@
-{
-    "experiment": "B",
-    "trainenv": {
-        "stop_on_collision": false,
-        "safe_actions_collision_method": null,
-        "abort_unsafe_collision_method": null
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "clip_ratio": 0.2,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 40,
-        "n_hidden_layers": 3,
-        "activation": 0,
-        "option": 0
-    },
-    "value": {
-        "learning_rate": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 3,
-        "n_hidden_layers_global": 2,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

best_configs/hail-etienne_expA.json

@@ -1,33 +0,0 @@
-{
-    "experiment": "A",
-    "trainenv": {
-        "stop_on_collision": false,
-        "safe_actions_collision_method": null,
-        "abort_unsafe_collision_method": null
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "clip_ratio": 0.2,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 20,
-        "n_hidden_layers": 4,
-        "activation": 0,
-        "option": 0
-    },
-    "value": {
-        "learning_rate": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 3,
-        "n_hidden_layers_global": 2,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

best_configs/shail-etienne2_expA.json

@@ -1,33 +0,0 @@
-{
-    "experiment": "A",
-    "trainenv": {
-        "stop_on_collision": false,
-        "safe_actions_collision_method": "circle",
-        "abort_unsafe_collision_method": "circle"
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "clip_ratio": 0.2,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 10,
-        "n_hidden_layers": 3,
-        "activation": 0,
-        "option": 0
-    },
-    "value": {
-        "learning_rate": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 3,
-        "n_hidden_layers_global": 2,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

best_configs/shail-etienne2_expB.json

@@ -1,33 +0,0 @@
-{
-    "experiment": "B",
-    "trainenv": {
-        "stop_on_collision": false,
-        "safe_actions_collision_method": "circle",
-        "abort_unsafe_collision_method": "circle"
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "clip_ratio": 0.2,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 10,
-        "n_hidden_layers": 3,
-        "activation": 0,
-        "option": 0
-    },
-    "value": {
-        "learning_rate": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 3,
-        "n_hidden_layers_global": 2,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

best_configs/shail-etienne3_expB.json

@@ -1,33 +0,0 @@
-{
-    "experiment": "B",
-    "trainenv": {
-        "stop_on_collision": false,
-        "safe_actions_collision_method": "circle",
-        "abort_unsafe_collision_method": "circle"
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "clip_ratio": 0.2,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 40,
-        "n_hidden_layers": 3,
-        "activation": 0,
-        "option": 0
-    },
-    "value": {
-        "learning_rate": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 3,
-        "n_hidden_layers_global": 2,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

best_configs/shail-etienne_expA.json

@@ -1,33 +0,0 @@
-{
-    "experiment": "A",
-    "trainenv": {
-        "stop_on_collision": false,
-        "safe_actions_collision_method": "circle",
-        "abort_unsafe_collision_method": "circle"
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "clip_ratio": 0.2,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 20,
-        "n_hidden_layers": 4,
-        "activation": 0,
-        "option": 0
-    },
-    "value": {
-        "learning_rate": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 3,
-        "n_hidden_layers_global": 2,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

config/networks.json5

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

config/value_dice.json5

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

eval_experiments.py

@@ -1,17 +1,23 @@
 import os
 from src.eval_main import eval_main
 from src.evaluation.utils import load_and_average
+import torch
+import json
 
-def main(method:str='expert', folder:str=None, locations=[(0,0)], skip_running=False):
+activations = [torch.nn.Tanh, torch.nn.LeakyReLU]
 
+def main(method:str='expert', folder:str=None, locations=[(0,0)], skip_running=False, save_videos:bool=False, videos_folder:str='videos', first_seed_only:bool=False):
+    
+    exclude_keys_from_policy_kwargs = {'learning_rate', 'learning_rate_decay', 'clip_ratio', 'iterations_per_epoch', 'option'}
     policy_kwargs = {}
-    if method in ['expert', 'idm']:
+
+    if method in ['expert', 'expert_agent', 'idm']:
         env, env_kwargs ='NRasterizedRouteIncrementingAgent', {}
     elif method in ['bc','gail']:
         env='NormalizedContinuousEvalEnv' 
         env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000}
     elif method in ['hail']:
-        env = 'NormalizedOptionsEvalEnv'
+        env = 'NormalizedSafeOptionsEvalEnv'
         env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000, 'safe_actions_collision_method': None, 'abort_unsafe_collision_method': None}
     elif method in ['shail']:
         env = 'NormalizedSafeOptionsEvalEnv'
@@ -23,7 +29,23 @@ def main(method:str='expert', folder:str=None, locations=[(0,0)], skip_running=F
 
     if folder is not None:
         files = [os.path.join(folder, f) for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]
-        print('%i folders found in %s folder' %(len(files), folder))
+        files = [f for f in files if f.endswith('.pt')]
+
+        if first_seed_only:
+            files = files[:1]
+
+        with open(os.path.join(folder, 'config.json'), 'rb') as f:
+            config = json.load(f)
+            
+        print('%i policy files found in %s folder' %(len(files), folder))
+        print('found policy config', config['policy'])
+
+        policy_config = {k: v for k, v in config['policy'].items() if k not in exclude_keys_from_policy_kwargs}
+        policy_config['activation'] = activations[policy_config['activation']]
+        print('final policy config', policy_config)
+
+        policy_kwargs.update(policy_config)
+        print('final policy kwargs', policy_kwargs)
 
     if not skip_running:
         for policy_file in files:
@@ -33,7 +55,8 @@ def main(method:str='expert', folder:str=None, locations=[(0,0)], skip_running=F
                                 policy_file=policy_file, 
                                 policy_kwargs=policy_kwargs,
                                 env=env, 
-                                env_kwargs=env_kwargs)
+                                env_kwargs=env_kwargs,
+                                videos_folder=None if not save_videos else videos_folder)
         outfolder = os.path.dirname(outbase)
     else:
         locstr = 'loc_'+'_'.join([f'r{ro}t{tr}' for (ro,tr) in locations])
@@ -60,7 +83,7 @@ def latex_print(am, light=False):
     print('success rate, distance travelled, RWSE_10, |DeltaV|, AccelJSD')
     if light:
         if 'rwse_10s' in am.keys():
-            print("%2.1f& %2.1f & %1.2f & %2.1f& "
+            print("%2.1f& %2.1f & %2.1f & %1.2f& "
             "%0.3f \\\\" %( 100*am['success rate'][0], am['mean travel distance'][0], am['rwse_10s'][0],
                             am['average absolute average velocity'][0],am['acceleration distribution divergence'][0] ))
             return
@@ -71,7 +94,7 @@ def latex_print(am, light=False):
         return
     
     print("%2.1f \\scriptstyle\\pm %2.1f & %2.1f \\scriptstyle\\pm %2.1f & "
-        "%1.2f \\scriptstyle\\pm %1.2f & %2.1f \\scriptstyle\\pm %1.1f & "
+        "%2.1f \\scriptstyle\\pm %1.1f & %1.2f \\scriptstyle\\pm %1.2f & "
         "%0.3f \\scriptstyle\\pm %0.3f \\\\" %( 100*am['success rate'][0], 100*am['success rate'][1],
                am['mean travel distance'][0] , am['mean travel distance'][1]  ,
                am['rwse_10s'][0] , am['rwse_10s'][1]  ,

evaluate_models.sh

@@ -1,19 +1,18 @@
-# can add --skip_running if you've run the runs before on the saved policies
+# can add --skip_running if you've already run the saved policies through the test environments and have appropriate
+# metrics in the out folder. Doing so will generate average metrics quickly.
 
+# Experiment A
 python -m eval_experiments 
-python -m eval_experiments --locations='[(0,4)]' 
 python -m eval_experiments --method idm 
-python -m eval_experiments --method idm --locations='[(0,4)]' 
 python -m eval_experiments --method bc --folder='test_policies/bc/expA' 
-python -m eval_experiments --method bc --folder='test_policies/bc/expB' --locations='[(0,4)]' 
 python -m eval_experiments --method gail --folder='test_policies/gail/expA' 
-python -m eval_experiments --method gail --folder='test_policies/gail/expB' --locations='[(0,4)]' 
 python -m eval_experiments --method hail --folder='test_policies/hail/expA'
-python -m eval_experiments --method hail --folder='test_policies/hail/expB' --locations='[(0,4)]'
 python -m eval_experiments --method shail --folder='test_policies/shail/expA' 
-python -m eval_experiments --method shail --folder='test_policies/shail/expB' --locations='[(0,4)]'
 
-python -m eval_experiments --method hail --folder='test_policies/hail-etienne/expA'
+# Experiment B
-python -m eval_experiments --method hail --folder='test_policies/hail-etienne/expB' --locations='[(0,4)]'
+python -m eval_experiments --locations='[(0,4)]' 
-python -m eval_experiments --method shail --folder='test_policies/shail-etienne/expA'
+python -m eval_experiments --method idm --locations='[(0,4)]' --skip_running
-python -m eval_experiments --method shail --folder='test_policies/shail-etienne/expB' --locations='[(0,4)]'
+python -m eval_experiments --method bc --folder='test_policies/bc/expB' --locations='[(0,4)]' --skip_running
+python -m eval_experiments --method gail --folder='test_policies/gail/expB' --locations='[(0,4)]' --skip_running
+python -m eval_experiments --method hail --folder='test_policies/hail/expB' --locations='[(0,4)]' --skip_running
+python -m eval_experiments --method shail --folder='test_policies/shail/expB' --locations='[(0,4)]' --skip_running

experiments/experiment.py

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

experiments/experiments.sh

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

experiments/train_vd.sh

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

gail-experiment.py

@@ -236,3 +236,5 @@ if __name__ == '__main__':
             check_dir = analysis._checkpoints[i]['logdir']
             shutil.copyfile(os.path.join(check_dir,'policy_final.pt'), 
                 os.path.join(savepath, f'policy_seed{s}.pt'))
+            shutil.copyfile(os.path.join(check_dir,'params.json'),
+                os.path.join(savepath, 'config.json')) # copy config automatically

generate_demos.sh

@@ -1,10 +0,0 @@
-#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]'

generate_videos.sh

@@ -0,0 +1,20 @@
+# can add --skip_running if you've already run the saved policies through the test environments and have appropriate
+# metrics in the out folder. Doing so will generate average metrics quickly.
+
+# Experiment A
+python -m eval_experiments 
+python -m eval_experiments --method expert_agent --save_videos --first_seed_only
+python -m eval_experiments --method idm --save_videos --first_seed_only
+python -m eval_experiments --method bc --folder='test_policies/bc/expA' --save_videos --first_seed_only
+python -m eval_experiments --method gail --folder='test_policies/gail/expA' --save_videos --first_seed_only
+python -m eval_experiments --method hail --folder='test_policies/hail/expA' --save_videos --first_seed_only
+python -m eval_experiments --method shail --folder='test_policies/shail/expA' --save_videos --first_seed_only
+
+# Experiment B
+python -m eval_experiments --locations='[(0,4)]' 
+python -m eval_experiments --method expert_agent --locations='[(0,4)]' --save_videos --first_seed_only
+python -m eval_experiments --method idm --locations='[(0,4)]' --save_videos --first_seed_only
+python -m eval_experiments --method bc --folder='test_policies/bc/expB' --locations='[(0,4)]' --save_videos --first_seed_only
+python -m eval_experiments --method gail --folder='test_policies/gail/expB' --locations='[(0,4)]' --save_videos --first_seed_only
+python -m eval_experiments --method hail --folder='test_policies/hail/expB' --locations='[(0,4)]' --save_videos --first_seed_only
+python -m eval_experiments --method shail --folder='test_policies/shail/expB' --locations='[(0,4)]' --save_videos --first_seed_only

intersimple-expert-rollout-setobs2.py

@@ -0,0 +1,65 @@
+import torch
+import functools
+from src.core.sampling import rollout_sb3
+from intersim.envs import IntersimpleLidarFlatIncrementingAgent
+from intersim.envs.intersimple import speed_reward
+from intersim.expert import NormalizedIntersimpleExpert
+from src.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)
+
+def main(track:int, loc:int=0):
+    env = IntersimpleLidarFlatIncrementingAgent(
+        loc=loc,
+        track=track,
+        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)
+    ))
+    print(env.nv, 'vehicles')
+    expert_data = rollout_sb3(env, policy, n_episodes=150, 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, f'intersimple-expert-data-setobs2-loc{loc}-track{track}.pt')
+
+def loop(tracks:list=[0]):
+    for track in tracks:
+        main(track)
+
+if __name__=='__main__':
+    import fire
+    fire.Fire(loop)

scratch/arec/intersimple/commands.txt

@@ -1,22 +0,0 @@
-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

@@ -1,145 +0,0 @@
-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

@@ -1,9 +0,0 @@
-#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

@@ -1,23 +0,0 @@
-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

@@ -1,48 +0,0 @@
-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

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

scratch/arec/intersimple/gail/test_discriminator.py

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

scratch/arec/intersimple/gail_image_multiagent_nocollision.py

@@ -1,70 +0,0 @@
-# %%
-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

@@ -1,70 +0,0 @@
-# %%
-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

@@ -1,172 +0,0 @@
-# %%
-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

@@ -1,559 +0,0 @@
-# %%
-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

@@ -1,510 +0,0 @@
-# %%
-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

@@ -1,55 +0,0 @@
-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

@@ -1,58 +0,0 @@
-
-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

@@ -1,11 +0,0 @@
-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/airl_flat.py

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

scratch/etienne/intersimple/bc_flat.py

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

scratch/etienne/intersimple/data/expert.py

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

scratch/etienne/intersimple/data/generate.sh

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

scratch/etienne/intersimple/gail/envs.py

@@ -1,46 +0,0 @@
-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

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

scratch/etienne/intersimple/gail_flat.py

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

scratch/etienne/intersimple/gail_flat_ray.py

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

scratch/etienne/intersimple/gail_image.py

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

scratch/etienne/intersimple/gail_image_multiagent_nocollision.py

@@ -1,70 +0,0 @@
-# %%
-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

@@ -1,79 +0,0 @@
-# %%
-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

@@ -1,171 +0,0 @@
-# %%
-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

@@ -1,70 +0,0 @@
-# %%
-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

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

scratch/etienne/intersimple/gail_options_image_alltracks.py

@@ -1,410 +0,0 @@
-# %%
-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

@@ -1,91 +0,0 @@
-# %%
-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

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

scratch/etienne/intersimple/imitation_quickstart.py

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

scratch/etienne/intersimple/ppo_const.py

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

scratch/etienne/intersimple/ppo_const_collision.py

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

scratch/etienne/intersimple/ppo_const_image.py

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

scratch/etienne/intersimple/ppo_const_image_random.py

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

scratch/etienne/intersimple/ppo_intersimple_tspeed.py

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

scratch/etienne/intersimple/ppo_speed.py

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

scratch/etienne/intersimple/ppo_speed_image.py

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

scratch/etienne/intersimple/ppo_speed_image_lowres.py

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