Update README.md

Optionally save videos of evaluated policies

README.md

@@ -1,7 +1,7 @@
 # InteractionImitation
 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)".
+Code for "[SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments](https://arxiv.org/abs/2204.01922)", which appeared at the 2023 International Conference on Robotics and Automation (ICRA).
 If you find this repository useful, please cite the paper:
 
 ```

cp-videos.sh

@@ -0,0 +1,10 @@
+# cp-videos videos/ videos/icra23/
+
+agents=( 5 27 39 43 47 53 63 81 83 87 93 96 105 113 124 127 130 134 )
+
+for a in "${agents[@]}"
+do
+    cp "$1/expert_agent/loc0/track0/agent${a}_ani.mp4" "$2/t${a}expert.mp4"
+    cp "$1/idm/loc0/track0/agent${a}_ani.mp4" "$2/t${a}idm.mp4"
+    cp "$1/shail/loc0/track0/agent${a}_ani.mp4" "$2/t${a}shail.mp4"
+done

eval_experiments.py

@@ -6,12 +6,14 @@ import json
 
 activations = [torch.nn.Tanh, torch.nn.LeakyReLU]
 
-def main(method:str='expert', folder:str=None, locations=[(0,0)], skip_running=False):
+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']:
+        env, env_kwargs ='NRasterizedRouteIncrementingAgent', {}
+    elif method in ['idm']:
         env, env_kwargs ='NRasterizedRouteIncrementingAgent', {'use_idm':True}
     elif method in ['bc','gail']:
         env='NormalizedContinuousEvalEnv' 
@@ -30,8 +32,13 @@ 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))]
         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'])
 
@@ -50,7 +57,8 @@ 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])

evaluate_models.sh

@@ -11,8 +11,8 @@ python -m eval_experiments --method shail --folder='test_policies/shail/expA'
 
 # Experiment B
 python -m eval_experiments --locations='[(0,4)]' 
-python -m eval_experiments --method idm --locations='[(0,4)]' --skip_running
-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
+python -m eval_experiments --method idm --locations='[(0,4)]'
+python -m eval_experiments --method bc --folder='test_policies/bc/expB' --locations='[(0,4)]'
+python -m eval_experiments --method gail --folder='test_policies/gail/expB' --locations='[(0,4)]'
+python -m eval_experiments --method hail --folder='test_policies/hail/expB' --locations='[(0,4)]'
+python -m eval_experiments --method shail --folder='test_policies/shail/expB' --locations='[(0,4)]'

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

src/baselines/rule_policies.py

@@ -172,6 +172,7 @@ class IDMRulePolicy(BaseAlgorithm):
 
             # Update environment interaction graph with leader
             self._env._env._graph._neighbor_dict={agent:[leader]}
+            self._env._update_graph = True
 
             delta_v = v_ego - v[leader, 0]
             d_des = self.d_min + self.tau * v_ego + v_ego * delta_v / (2* (self.a_max*self.b_pref)**0.5 )

src/eval_main.py

@@ -5,6 +5,7 @@ import intersim
 from intersim.envs import Intersimple
 from stable_baselines3.common.base_class import BaseAlgorithm
 from src.baselines import IDMRulePolicy
+from src.data.expert import NormalizedIntersimpleExpert
 from src.evaluation import IntersimpleEvaluation
 import src.gail.options as options_envs
 from src.evaluation.metrics import divergence, visualize_distribution, rwse
@@ -40,6 +41,8 @@ def load_policy(method:str,
     ml = torch.device('cpu') if not torch.cuda.is_available() else None
     if method == 'idm':
         policy = IDMRulePolicy(env, **policy_kwargs)
+    elif method == 'expert_agent':
+        policy = NormalizedIntersimpleExpert(env, **policy_kwargs)
     elif method == 'bc':
         policy = SetPolicy(env.action_space.shape[-1], **policy_kwargs)
         policy.load_state_dict(torch.load(policy_file, map_location=ml))
@@ -177,7 +180,8 @@ def evaluate_policy(locations:List[Tuple[int,int]],
     env_kwargs:dict,
     method: str,
     policy_file: str,
-    policy_kwargs:dict) -> List[Dict[str,list]]:
+    policy_kwargs:dict,
+    videos_folder: Optional[str] = None) -> List[Dict[str,list]]:
     """
     Evaluate policy on an incrementing agent environment at all locations. 
     Return metrics for that policy
@@ -230,7 +234,13 @@ def evaluate_policy(locations:List[Tuple[int,int]],
         policy = load_policy(method, policy_file, policy_kwargs, eval_env)
 
         # run policy on environment
-        policy_metrics[i] = evaluator.evaluate(policy)
+        policy_videos_folder = None
+        if videos_folder is not None:
+            policy_videos_folder = os.path.join(videos_folder, method, f'loc{iround}', f'track{track}')
+            os.makedirs(policy_videos_folder, exist_ok=True)
+        policy_metrics[i] = evaluator.evaluate(
+            policy, videos_folder=policy_videos_folder
+        )
     
     return policy_metrics
         
@@ -364,7 +374,8 @@ def eval_main(
     policy_kwargs: dict={},
     env: str='NRasterizedRouteIncrementingAgent', 
     env_kwargs: dict={},
-    seed: int=0):
+    seed: int=0,
+    videos_folder: Optional[str]=None):
     """
     Test a particular model at different testing locations/tracks and compute average metrics 
     over all files. 
@@ -410,7 +421,7 @@ def eval_main(
     else:
 
         # evaluate it on the given roundabouts
-        policy_metrics = evaluate_policy(locations, env, env_kwargs, method, policy_file, policy_kwargs)
+        policy_metrics = evaluate_policy(locations, env, env_kwargs, method, policy_file, policy_kwargs, videos_folder=videos_folder)
         smetrics = summary_metrics(policy_metrics)
         save_metrics(smetrics, outbase+'_summary.pkl')
         cmetrics = comparison_metrics(policy_metrics, expert_metrics, outbase=outbase)

src/evaluation/evaluation.py

@@ -9,6 +9,8 @@ from tqdm import tqdm
 from src.util.wrappers import IntersimpleTimeLimit
 from src.options.envs import OptionsEnv
 from src.safe_options.options import SafeOptionsEnv
+from src.evaluation.vec_env import CallbackWhenDoneVecEnv
+import matplotlib.pyplot as plt
 
 class IntersimpleEvaluation:
     """
@@ -80,7 +82,7 @@ class IntersimpleEvaluation:
         with open(filestr, 'wb') as f:
             pickle.dump(self._metrics, f)     
 
-    def evaluate(self, policy, filestr: Optional[str] = None) -> Dict[str, list]:
+    def evaluate(self, policy, filestr: Optional[str] = None, videos_folder: Optional[str] = None) -> Dict[str, list]:
         """
         Evaluate a policy on the incrementing agent evaluation environment
         
@@ -88,6 +90,8 @@ class IntersimpleEvaluation:
             policy (BaseClass.BaseAlgorithm): policy in which policy.predict(observation)[0] returns an action
             filestr (str): path-like string to dump metrics to or None
         """
+        self.videos_folder = videos_folder
+
         self.reset()
         if self.use_pbar:
             self.pbar = tqdm(total=self.n_episodes)
@@ -97,10 +101,11 @@ class IntersimpleEvaluation:
 
         evaluate_policy(
             policy, 
-            self.env,
+            self.env if self.videos_folder is None else CallbackWhenDoneVecEnv([lambda: self.env], self.done_callback),
             n_eval_episodes=self.n_episodes,
             callback=self.evaluate_options_policy_callback if self.is_options_env else self.evaluate_policy_callback,
-            return_episode_rewards=False
+            return_episode_rewards=False,
+            render=self.videos_folder is not None,
         )
         if self.use_pbar:
             self.pbar.close()
@@ -146,6 +151,14 @@ class IntersimpleEvaluation:
         if done and self.use_pbar:
             self.pbar.update(1)
         
+    def done_callback(self, info):
+        if self.is_options_env:
+            info = info['ll']['infos'][0]
+        agent = info['agent']
+        filestr = os.path.join(self.videos_folder, f'agent{agent}')
+        self.env.close(filestr=filestr)
+        plt.close('all')
+    
     def post_proc(self):
         """
         Postprocess and metrics after simulation episodes

src/evaluation/vec_env.py

@@ -0,0 +1,30 @@
+from stable_baselines3.common.vec_env import DummyVecEnv
+from stable_baselines3.common.vec_env.base_vec_env import VecEnvStepReturn
+from copy import deepcopy
+import numpy as np
+
+class CallbackWhenDoneVecEnv(DummyVecEnv):
+    """DummyVecEnv that calls `done_callback` before resetting the wrapped environment."""
+
+    def __init__(self, env_fns, done_callback):
+        assert len(env_fns) == 1 # for now
+        super().__init__(env_fns)
+        self.done_callback = done_callback
+
+    def step_wait(self) -> VecEnvStepReturn:
+        for env_idx in range(self.num_envs):
+            obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(
+                self.actions[env_idx]
+            )
+            if self.buf_dones[env_idx]:
+                # save final observation where user can get it, then reset
+                self.buf_infos[env_idx]["terminal_observation"] = obs
+
+                self.done_callback(deepcopy(self.buf_infos[env_idx]))
+                
+                obs = self.envs[env_idx].reset()
+            self._save_obs(env_idx, obs)
+        return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones), deepcopy(self.buf_infos))
+    
+    def render(self, mode='post'):
+        super().render(mode)

src/options/envs.py

@@ -45,6 +45,7 @@ class OptionsEnv(Wrapper):
         self.options = options
         self.action_space = gym.spaces.Discrete(len(options))
         self.max_plan_length = max(t for _, t in options)
+        self.render_mode = None
     
     def plan(self, option):
         target_v, t = option
@@ -85,10 +86,13 @@ class OptionsEnv(Wrapper):
         n_steps = k + 1
         return observations, actions, rewards, env_done, plan_done, infos, n_steps
     
-    def step(self, action, render_mode=None):
+    def render(self, mode='post'):
+        self.render_mode = mode
+
+    def step(self, action):
         a = int(action)
         assert a == action
-        ll_obs, ll_actions, ll_rewards, ll_env_done, ll_plan_done, ll_infos, ll_steps = self.execute_plan(self.last_obs, self.options[a], render_mode)
+        ll_obs, ll_actions, ll_rewards, ll_env_done, ll_plan_done, ll_infos, ll_steps = self.execute_plan(self.last_obs, self.options[a], self.render_mode)
         hl_obs = ll_obs[ll_steps]
         hl_reward = (ll_rewards * ~ll_plan_done).sum().item()
         hl_done = ll_env_done[ll_steps-1].item()

src/safe_options/options.py

@@ -225,8 +225,8 @@ class SafeOptionsEnv(OptionsEnv):
         }
         return obs
 
-    def step(self, action, render_mode=None):
-        obs, reward, done, info = super().step(action, render_mode)
+    def step(self, action):
+        obs, reward, done, info = super().step(action)
         obs = {
             'observation': obs,
             'safe_actions': self.safe_actions(),

src/util/wrappers.py

@@ -5,14 +5,23 @@ class Wrapper(gym.Wrapper):
     def __getattr__(self, name):
         return getattr(self.env, name)
 
+    def close(self, *args, **kwargs):
+        return self.env.close(*args, **kwargs)
+
 class TransformObservation(gym.wrappers.TransformObservation):
     def __getattr__(self, name):
         return getattr(self.env, name)
 
+    def close(self, *args, **kwargs):
+        return self.env.close(*args, **kwargs)
+
 class IntersimpleTimeLimit(gym.wrappers.TimeLimit):
     def __getattr__(self, name):
         return getattr(self.env, name)
 
+    def close(self, *args, **kwargs):
+        return self.env.close(*args, **kwargs)
+
 class CollisionPenaltyWrapper(Wrapper):
 
     def __init__(self, env, collision_distance, collision_penalty, *args, **kwargs):

test_policies/gail/expA/config.json

@@ -1,32 +1,31 @@
 {
-  "discriminator": {
-    "activation": 0,
-    "hidden_layer_size": 10,
-    "iterations_per_epoch": 100,
-    "learning_rate": 0.001,
-    "n_hidden_layers_element": 4,
-    "n_hidden_layers_global": 1,
-    "weight_decay": 0.0001
-  },
     "experiment": "A",
+    "trainenv": {
+        "stop_on_collision": false
+    },
     "policy": {
-    "activation": 0,
-    "clip_ratio": 0.2,
-    "hidden_layer_size": 40,
-    "iterations_per_epoch": 100,
         "learning_rate": 0.0003,
         "learning_rate_decay": 1.0,
-    "n_hidden_layers": 2
-  },
-  "seed": 5,
-  "train_epochs": 100,
-  "trainenv": {
-    "stop_on_collision": false,
-    "use_idm": true
+        "clip_ratio": 0.2,
+        "iterations_per_epoch": 100,
+        "hidden_layer_size": 40,
+        "n_hidden_layers": 2,
+        "activation": 0
     },
     "value": {
-    "iterations_per_epoch": 1000,
         "learning_rate": 0.0001,
-    "weight_decay": 0.001
-  }
+        "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
 }

test_policies/gail/expB/config.json

@@ -1,32 +1,31 @@
 {
-  "discriminator": {
-    "activation": 0,
-    "hidden_layer_size": 10,
-    "iterations_per_epoch": 100,
-    "learning_rate": 0.001,
-    "n_hidden_layers_element": 4,
-    "n_hidden_layers_global": 1,
-    "weight_decay": 0.0001
-  },
     "experiment": "B",
+    "trainenv": {
+        "stop_on_collision": false
+    },
     "policy": {
-    "activation": 0,
-    "clip_ratio": 0.2,
-    "hidden_layer_size": 40,
-    "iterations_per_epoch": 100,
         "learning_rate": 0.0003,
         "learning_rate_decay": 1.0,
-    "n_hidden_layers": 2
-  },
-  "seed": 4,
-  "train_epochs": 100,
-  "trainenv": {
-    "stop_on_collision": false,
-    "use_idm": true
+        "clip_ratio": 0.2,
+        "iterations_per_epoch": 100,
+        "hidden_layer_size": 40,
+        "n_hidden_layers": 2,
+        "activation": 0
     },
     "value": {
-    "iterations_per_epoch": 1000,
         "learning_rate": 0.0001,
-    "weight_decay": 0.001
-  }
+        "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
 }

test_policies/shail/expA/config.json

@@ -1,34 +1,33 @@
 {
-  "discriminator": {
-    "activation": 0,
-    "hidden_layer_size": 10,
-    "iterations_per_epoch": 100,
-    "learning_rate": 0.001,
-    "n_hidden_layers_element": 4,
-    "n_hidden_layers_global": 1,
-    "weight_decay": 0.0001
-  },
     "experiment": "A",
+    "trainenv": {
+        "stop_on_collision": false,
+        "safe_actions_collision_method": "circle",
+        "abort_unsafe_collision_method": "circle"
+    },
     "policy": {
-    "activation": 0,
-    "clip_ratio": 0.2,
-    "hidden_layer_size": 40,
-    "iterations_per_epoch": 100,
         "learning_rate": 0.0003,
         "learning_rate_decay": 1.0,
+        "clip_ratio": 0.2,
+        "iterations_per_epoch": 100,
+        "hidden_layer_size": 40,
         "n_hidden_layers": 2,
+        "activation": 0,
         "option": 0
     },
-  "seed": 3,
-  "train_epochs": 90,
-  "trainenv": {
-    "abort_unsafe_collision_method": "circle",
-    "safe_actions_collision_method": "circle",
-    "stop_on_collision": false,
-    "use_idm": true
-  },
     "value": {
-    "iterations_per_epoch": 1000,
-    "learning_rate": 0.001
-  }
+        "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": 4,
+        "n_hidden_layers_global": 1,
+        "hidden_layer_size": 10,
+        "activation": 0
+    },
+    "train_epochs": 90,
+    "seed": 0
 }

test_policies/shail/expB/config.json

@@ -1,34 +1,33 @@
 {
-  "discriminator": {
-    "activation": 0,
-    "hidden_layer_size": 10,
-    "iterations_per_epoch": 100,
-    "learning_rate": 0.001,
-    "n_hidden_layers_element": 4,
-    "n_hidden_layers_global": 2,
-    "weight_decay": 0.0001
-  },
     "experiment": "B",
+    "trainenv": {
+        "stop_on_collision": false,
+        "safe_actions_collision_method": "circle",
+        "abort_unsafe_collision_method": "circle"
+    },
     "policy": {
-    "activation": 0,
-    "clip_ratio": 0.2,
-    "hidden_layer_size": 20,
-    "iterations_per_epoch": 100,
         "learning_rate": 0.0003,
         "learning_rate_decay": 1.0,
+        "clip_ratio": 0.2,
+        "iterations_per_epoch": 100,
+        "hidden_layer_size": 20,
         "n_hidden_layers": 2,
+        "activation": 0,
         "option": 0
     },
-  "seed": 3,
-  "train_epochs": 85,
-  "trainenv": {
-    "abort_unsafe_collision_method": "circle",
-    "safe_actions_collision_method": "circle",
-    "stop_on_collision": false,
-    "use_idm": true
-  },
     "value": {
-    "iterations_per_epoch": 1000,
-    "learning_rate": 0.001
-  }
+        "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": 4,
+        "n_hidden_layers_global": 2,
+        "hidden_layer_size": 10,
+        "activation": 0
+    },
+    "train_epochs": 85,
+    "seed": 0
 }