Polish code

pyproject.toml

@@ -83,7 +83,7 @@ filterwarnings = [
     # Tensorboard warnings
     "ignore::DeprecationWarning:tensorboard",
     # Gymnasium warnings
-    # "ignore::UserWarning:gym",
+    "ignore::UserWarning:gymnasium",
     # "ignore::DeprecationWarning:.*passive_env_checker.*",
 ]
 markers = [

stable_baselines3/common/env_checker.py

@@ -250,7 +250,7 @@ def _check_returned_values(env: gym.Env, observation_space: spaces.Space, action
     assert isinstance(reset_returns, tuple), "`reset()` must return a tuple (obs, info)"
     assert len(reset_returns) == 2, f"`reset()` must return a tuple of size 2 (obs, info), not {len(reset_returns)}"
     obs, info = reset_returns
-    assert isinstance(info, dict), "The second element of the tuple return by `reset()` must be a dictionary"
+    assert isinstance(info, dict), f"The second element of the tuple return by `reset()` must be a dictionary not {info}"
 
     if _is_goal_env(env):
         # Make mypy happy, already checked
@@ -277,7 +277,10 @@ def _check_returned_values(env: gym.Env, observation_space: spaces.Space, action
     action = action_space.sample()
     data = env.step(action)
 
-    assert len(data) == 5, "The `step()` method must return four values: obs, reward, terminated, truncated, info"
+    assert len(data) == 5, (
+        "The `step()` method must return five values: "
+        f"obs, reward, terminated, truncated, info. Actual: {len(data)} values returned."
+    )
 
     # Unpack
     obs, reward, terminated, truncated, info = data

stable_baselines3/common/preprocessing.py

@@ -202,9 +202,9 @@ def get_action_dim(action_space: spaces.Space) -> int:
         return int(len(action_space.nvec))
     elif isinstance(action_space, spaces.MultiBinary):
         # Number of binary actions
-        assert isinstance(action_space.n, int), (
-            "Multi-dimensional MultiBinary action space is not supported. " "You can flatten it instead."
-        )
+        assert isinstance(
+            action_space.n, int
+        ), "Multi-dimensional MultiBinary action space is not supported. You can flatten it instead."
         return int(action_space.n)
     else:
         raise NotImplementedError(f"{action_space} action space is not supported")

tests/test_buffers.py

@@ -34,7 +34,8 @@ def step(self, action):
         self._t += 1
         index = self._t % len(self._observations)
         obs = self._observations[index]
-        terminated = truncated = self._t >= self._ep_length
+        terminated = False
+        truncated = self._t >= self._ep_length
         reward = self._rewards[index]
         return obs, reward, terminated, truncated, {}
 
@@ -63,7 +64,8 @@ def step(self, action):
         self._t += 1
         index = self._t % len(self._observations)
         obs = {key: self._observations[index] for key in self.observation_space.spaces.keys()}
-        terminated = truncated = self._t >= self._ep_length
+        terminated = False
+        truncated = self._t >= self._ep_length
         reward = self._rewards[index]
         return obs, reward, terminated, truncated, {}
 

tests/test_vec_envs.py

@@ -42,7 +42,8 @@ def step(self, action):
         reward = float(np.random.rand())
         self._choose_next_state()
         self.current_step += 1
-        terminated = truncated = self.current_step >= self.ep_length
+        terminated = False
+        truncated = self.current_step >= self.ep_length
         return self.state, reward, terminated, truncated, {}
 
     def _choose_next_state(self):
@@ -178,7 +179,8 @@ def reset(self):
     def step(self, action):
         prev_step = self.current_step
         self.current_step += 1
-        terminated = truncated = self.current_step >= self.max_steps
+        terminated = False
+        truncated = self.current_step >= self.max_steps
         return np.array([prev_step], dtype="int"), 0.0, terminated, truncated, {}
 
 

tests/test_vec_normalize.py

@@ -35,7 +35,8 @@ def step(self, action):
         self.t += 1
         index = (self.t + self.return_reward_idx) % len(self.returned_rewards)
         returned_value = self.returned_rewards[index]
-        terminated = truncated = self.t == len(self.returned_rewards)
+        terminated = False
+        truncated = self.t == len(self.returned_rewards)
         return np.array([returned_value]), returned_value, terminated, truncated, {}
 
     def reset(self, *, seed: Optional[int] = None, options: Optional[Dict] = None):
@@ -69,8 +70,8 @@ def reset(self, *, seed: Optional[int] = None, options: Optional[Dict] = None):
     def step(self, action):
         obs = self.observation_space.sample()
         reward = self.compute_reward(obs["achieved_goal"], obs["desired_goal"], {})
-        done = np.random.rand() > 0.8
-        return obs, reward, done, False, {}
+        terminated = np.random.rand() > 0.8
+        return obs, reward, terminated, False, {}
 
     def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, _info) -> np.float32:
         distance = np.linalg.norm(achieved_goal - desired_goal, axis=-1)
@@ -100,8 +101,8 @@ def reset(self, *, seed: Optional[int] = None, options: Optional[Dict] = None):
 
     def step(self, action):
         obs = self.observation_space.sample()
-        done = np.random.rand() > 0.8
-        return obs, 0.0, done, False, {}
+        terminated = np.random.rand() > 0.8
+        return obs, 0.0, terminated, False, {}
 
 
 def allclose(obs_1, obs_2):