Make RecordEpisodeStatistics work with VectorEnv

gym/wrappers/record_episode_statistics.py

@@ -1,39 +1,59 @@
 import time
 from collections import deque
+import numpy as np
 import gym
 
 
 class RecordEpisodeStatistics(gym.Wrapper):
     def __init__(self, env, deque_size=100):
         super(RecordEpisodeStatistics, self).__init__(env)
+        self.env_is_vec = True
+        if not isinstance(env, gym.vector.VectorEnv):
+            self.num_envs = 1
+            self.env_is_vec = False
         self.t0 = (
             time.time()
         )  # TODO: use perf_counter when gym removes Python 2 support
-        self.episode_return = 0.0
-        self.episode_length = 0
+        self.episode_count = 0
+        self.episode_returns = None
+        self.episode_lengths = None
         self.return_queue = deque(maxlen=deque_size)
         self.length_queue = deque(maxlen=deque_size)
 
     def reset(self, **kwargs):
-        observation = super(RecordEpisodeStatistics, self).reset(**kwargs)
-        self.episode_return = 0.0
-        self.episode_length = 0
-        return observation
+        observations = super(RecordEpisodeStatistics, self).reset(**kwargs)
+        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)
+        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)
+        return observations
 
     def step(self, action):
-        observation, reward, done, info = super(RecordEpisodeStatistics, self).step(
+        observations, rewards, dones, infos = super(RecordEpisodeStatistics, self).step(
             action
         )
-        self.episode_return += reward
-        self.episode_length += 1
-        if done:
-            info["episode"] = {
-                "r": self.episode_return,
-                "l": self.episode_length,
-                "t": round(time.time() - self.t0, 6),
-            }
-            self.return_queue.append(self.episode_return)
-            self.length_queue.append(self.episode_length)
-            self.episode_return = 0.0
-            self.episode_length = 0
-        return observation, reward, done, info
+        self.episode_returns += rewards
+        self.episode_lengths += 1
+        if not self.env_is_vec:
+            infos = [infos]
+            dones = [dones]
+        for i in range(len(dones)):
+            if dones[i]:
+                infos[i] = infos[i].copy()
+                episode_return = self.episode_returns[i]
+                episode_length = self.episode_lengths[i]
+                episode_info = {
+                    "r": episode_return,
+                    "l": episode_length,
+                    "t": round(time.time() - self.t0, 6),
+                }
+                infos[i]["episode"] = episode_info
+                self.return_queue.append(episode_return)
+                self.length_queue.append(episode_length)
+                self.episode_count += 1
+                self.episode_returns[i] = 0
+                self.episode_lengths[i] = 0
+        return (
+            observations,
+            rewards,
+            dones if self.env_is_vec else dones[0],
+            infos if self.env_is_vec else infos[0],
+        )

gym/wrappers/test_record_episode_statistics.py

@@ -12,8 +12,8 @@ def test_record_episode_statistics(env_id, deque_size):
 
     for n in range(5):
         env.reset()
-        assert env.episode_return == 0.0
-        assert env.episode_length == 0
+        assert env.episode_returns[0] == 0.0
+        assert env.episode_lengths[0] == 0
         for t in range(env.spec.max_episode_steps):
             _, _, done, info = env.step(env.action_space.sample())
             if done:
@@ -22,3 +22,17 @@ def test_record_episode_statistics(env_id, deque_size):
                 break
     assert len(env.return_queue) == deque_size
     assert len(env.length_queue) == deque_size
+
+
+@pytest.mark.parametrize("env_id", ["CartPole-v0"])
+def test_record_episode_statistics_with_vectorenv(env_id):
+    envs = gym.vector.make(env_id, asynchronous=False)
+    envs = RecordEpisodeStatistics(envs)
+    envs.reset()
+    for _ in range(envs.env.envs[0].spec.max_episode_steps + 1):
+        _, _, dones, infos = envs.step(envs.action_space.sample())
+        for idx, info in enumerate(infos):
+            if dones[idx]:
+                assert "episode" in info
+                assert all([item in info["episode"] for item in ["r", "l", "t"]])
+                break