committing learned policies and summary files

README.md

@@ -1,10 +1,22 @@
 # InteractionImitation
-Imitation Learning with the INTERACTION Dataset
+Imitation Learning with the [Interaction Dataset](https://interaction-dataset.com/) via the [InteractionSimulator](https://github.com/sisl/InteractionSimulator) gym environments.
+
+Code for "[SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments](https://arxiv.org/abs/2204.01922)".
+If you find this repository useful, please cite the paper:
+
+```
+@article{jamgochian2022shail,
+  author = {Arec Jamgochian and Etienne Buehrle and Johannes Fischer and Mykel J. Kochenderfer},
+  title = {{SHAIL}: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments},
+  journal = {arXiv:2204.01922 [cs]},
+  year = {2022}
+}
+```
 
 ## Getting started
-Clone InteractionSimulator and pip install the module.
+Clone the `InteractionSimulator` with the `shail` tag and pip install the module.
 ```
-git clone https://github.com/sisl/InteractionSimulator.git
+git clone --branch shail https://github.com/sisl/InteractionSimulator.git
 cd InteractionSimulator
 pip install -e .
 cd ..
@@ -19,53 +31,28 @@ 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

@@ -55,6 +55,7 @@ def training_function(config):
                 ),
                 check_collisions=True,
                 stop_on_collision=config['trainenv']['stop_on_collision'],
+                use_idm=config['trainenv']['use_idm'],
             ), collision_distance=6, collision_penalty=100),
             lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
         )) for _ in range(60)]
@@ -68,6 +69,8 @@ def training_function(config):
                 ),
                 check_collisions=True,
                 stop_on_collision=config['trainenv']['stop_on_collision'],
+                use_idm=config['trainenv']['use_idm'],
+                track=track,
             ), collision_distance=6, collision_penalty=100),
             lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
         )) for _ in range(15)] for track in range(4)],[])
@@ -142,7 +145,7 @@ 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')
@@ -159,13 +162,14 @@ if __name__ == '__main__':
                 'experiment': args.train,
                 'trainenv': {
                     'stop_on_collision': False, 
+                    'use_idm':True,
                 },
                 'policy': {
                     'learning_rate': 3e-4, 
-                    'learning_rate_decay': tune.grid_search([0.001, 1.0]), 
+                    'learning_rate_decay': tune.grid_search([0.999, 1.0]), 
-                    'hidden_layer_size': tune.grid_search([10, 20, 40, 80]),
+                    'hidden_layer_size': tune.grid_search([10, 20, 40]),
-                    'n_hidden_layers': tune.grid_search([2, 3, 4]), 
+                    'n_hidden_layers': tune.grid_search([2, 3]), 
-                    'activation':0,
+                    'activation':tune.grid_search([0, 1]),
                 },
                 'train_epochs': args.epochs,
                 'seed': 0,
@@ -209,4 +213,6 @@ if __name__ == '__main__':
             s = analysis._checkpoints[i]['config']['seed']
             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'))
+                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/bc_expA.json

@@ -1,7 +1,8 @@
 {
     "experiment": "A",
     "trainenv": {
-        "stop_on_collision": false
+        "stop_on_collision": false,
+        "use_idm": true
     },
     "policy": {
         "learning_rate": 0.0003,

best_configs/bc_expB.json

@@ -1,7 +1,8 @@
 {
     "experiment": "B",
     "trainenv": {
-        "stop_on_collision": false
+        "stop_on_collision": false,
+        "use_idm": true
     },
     "policy": {
         "learning_rate": 0.0003,

best_configs/gail-trpo_expA.json

@@ -1,31 +0,0 @@
-{
-    "experiment": "A",
-    "trainenv": {
-        "stop_on_collision": false
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "delta": 0.01,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 40,
-        "n_hidden_layers": 2,
-        "activation": 0
-    },
-    "value": {
-        "learning_rate": 0.0001,
-        "weight_decay": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 4,
-        "n_hidden_layers_global": 1,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

best_configs/gail-trpo_expB.json

@@ -1,31 +0,0 @@
-{
-    "experiment": "B",
-    "trainenv": {
-        "stop_on_collision": false
-    },
-    "policy": {
-        "learning_rate": 0.0003,
-        "learning_rate_decay": 1.0,
-        "delta": 0.01,
-        "iterations_per_epoch": 100,
-        "hidden_layer_size": 40,
-        "n_hidden_layers": 2,
-        "activation": 0
-    },
-    "value": {
-        "learning_rate": 0.0001,
-        "weight_decay": 0.001,
-        "iterations_per_epoch": 1000
-    },
-    "discriminator": {
-        "learning_rate": 0.001,
-        "weight_decay": 0.0001,
-        "iterations_per_epoch": 100,
-        "n_hidden_layers_element": 4,
-        "n_hidden_layers_global": 1,
-        "hidden_layer_size": 10,
-        "activation": 0
-    },
-    "train_epochs": 100,
-    "seed": 0
-}

best_configs/gail_expA.json

@@ -1,7 +1,8 @@
 {
     "experiment": "A",
     "trainenv": {
-        "stop_on_collision": false
+        "stop_on_collision": false,
+        "use_idm": true
     },
     "policy": {
         "learning_rate": 0.0003,

best_configs/gail_expB.json

@@ -1,7 +1,8 @@
 {
     "experiment": "B",
     "trainenv": {
-        "stop_on_collision": false
+        "stop_on_collision": false,
+        "use_idm": true
     },
     "policy": {
         "learning_rate": 0.0003,

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_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": 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-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/hail-etienne_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": 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/hail_expA.json

@@ -3,7 +3,8 @@
     "trainenv": {
         "stop_on_collision": false,
         "safe_actions_collision_method": null,
-        "abort_unsafe_collision_method": null
+        "abort_unsafe_collision_method": null,
+        "use_idm": true
     },
     "policy": {
         "learning_rate": 0.0003,

best_configs/hail_expB.json

@@ -3,7 +3,8 @@
     "trainenv": {
         "stop_on_collision": false,
         "safe_actions_collision_method": null,
-        "abort_unsafe_collision_method": null
+        "abort_unsafe_collision_method": null,
+        "use_idm": true
     },
     "policy": {
         "learning_rate": 0.0003,

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_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": 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-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-etienne4_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": 500,
-        "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": 150,
-    "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
-}

best_configs/shail-etienne_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": 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_expA.json

@@ -3,7 +3,8 @@
     "trainenv": {
         "stop_on_collision": false,
         "safe_actions_collision_method": "circle",
-        "abort_unsafe_collision_method": "circle"
+        "abort_unsafe_collision_method": "circle",
+        "use_idm": true
     },
     "policy": {
         "learning_rate": 0.0003,

best_configs/shail_expB.json

@@ -3,7 +3,8 @@
     "trainenv": {
         "stop_on_collision": false,
         "safe_actions_collision_method": "circle",
-        "abort_unsafe_collision_method": "circle"
+        "abort_unsafe_collision_method": "circle",
+        "use_idm": true
     },
     "policy": {
         "learning_rate": 0.0003,

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

@@ -12,16 +12,16 @@ def main(method:str='expert', folder:str=None, locations=[(0,0)], skip_running=F
     policy_kwargs = {}
 
     if method in ['expert', 'idm']:
-        env, env_kwargs ='NRasterizedRouteIncrementingAgent', {}
+        env, env_kwargs ='NRasterizedRouteIncrementingAgent', {'use_idm':True}
     elif method in ['bc','gail']:
         env='NormalizedContinuousEvalEnv' 
-        env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000}
+        env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000, 'use_idm':True}
     elif method in ['hail']:
         env = 'NormalizedSafeOptionsEvalEnv'
-        env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000, 'safe_actions_collision_method': None, 'abort_unsafe_collision_method': None}
+        env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000, 'safe_actions_collision_method': None, 'abort_unsafe_collision_method': None, 'use_idm':True}
     elif method in ['shail']:
         env = 'NormalizedSafeOptionsEvalEnv'
-        env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000}
+        env_kwargs={'stop_on_collision':True, 'max_episode_steps':1000, 'use_idm':True}
     else:
         raise NotImplementedError
 
@@ -35,12 +35,12 @@ def main(method:str='expert', folder:str=None, locations=[(0,0)], skip_running=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 = {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']]
+        policy_config['activation'] = activations[policy_config['activation']]
-    print('final policy config', policy_config)
+        print('final policy config', policy_config)
 
-    policy_kwargs.update(policy_config)
+        policy_kwargs.update(policy_config)
-    print('final policy kwargs', policy_kwargs)
+        print('final policy kwargs', policy_kwargs)
 
     if not skip_running:
         for policy_file in files:
@@ -77,7 +77,7 @@ 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
@@ -88,7 +88,7 @@ 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/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

@@ -53,6 +53,7 @@ def training_function(config):
                 ),
                 check_collisions=True,
                 stop_on_collision=config['trainenv']['stop_on_collision'],
+                use_idm=config['trainenv']['use_idm'],
             ), collision_distance=6, collision_penalty=100),
             lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
         )) for _ in range(60)]
@@ -67,6 +68,7 @@ def training_function(config):
                 ),
                 check_collisions=True,
                 stop_on_collision=config['trainenv']['stop_on_collision'],
+                use_idm=config['trainenv']['use_idm'],
                 track=track,
             ), collision_distance=6, collision_penalty=100),
             lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10)
@@ -169,7 +171,8 @@ if __name__ == '__main__':
             config={
                 'experiment': args.train,
                 'trainenv': {
-                    'stop_on_collision': False, 
+                    'stop_on_collision': False,
+                    'use_idm': True, 
                 },
                 'policy': {
                     'learning_rate': 3e-4, 
@@ -235,4 +238,6 @@ if __name__ == '__main__':
             s = analysis._checkpoints[i]['config']['seed']
             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'))
+                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]'

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)