Fix env checker single-step-env edge case (#1521)

docs/misc/changelog.rst

@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 2.0.0a10 (WIP)
+Release 2.0.0a11 (WIP)
 --------------------------
 
 **Gymnasium support**
@@ -39,6 +39,8 @@ Bug Fixes:
 - Fixed ``VecExtractDictObs`` does not handle terminal observation (@WeberSamuel)
 - Set NumPy version to ``>=1.20`` due to use of ``numpy.typing`` (@troiganto)
 - Fixed loading DQN changes ``target_update_interval`` (@tobirohrer)
+- Fixed env checker to properly reset the env before calling ``step()`` when checking
+  for ``Inf`` and ``NaN`` (@lutogniew)
 
 Deprecations:
 ^^^^^^^^^^^^^
@@ -1346,3 +1348,4 @@ And all the contributors:
 @Melanol @qgallouedec @francescoluciano @jlp-ue @burakdmb @timothe-chaumont @honglu2875
 @anand-bala @hughperkins @sidney-tio @AlexPasqua @dominicgkerr @Akhilez @Rocamonde @tobirohrer @ZikangXiong
 @DavyMorgan @luizapozzobon @Bonifatius94 @theSquaredError @harveybellini @DavyMorgan @FieteO @jonasreiher @npit @WeberSamuel @troiganto
+@lutogniew

stable_baselines3/common/env_checker.py

@@ -110,6 +110,7 @@ def _check_unsupported_spaces(env: gym.Env, observation_space: spaces.Space, act
 def _check_nan(env: gym.Env) -> None:
     """Check for Inf and NaN using the VecWrapper."""
     vec_env = VecCheckNan(DummyVecEnv([lambda: env]))
+    vec_env.reset()
     for _ in range(10):
         action = np.array([env.action_space.sample()])
         _, _, _, _ = vec_env.step(action)

stable_baselines3/version.txt

@@ -1 +1 @@
-2.0.0a10
+2.0.0a11

tests/test_env_checker.py

@@ -1,4 +1,4 @@
-from typing import Dict, Optional
+from typing import Any, Dict, Optional, Tuple
 
 import gymnasium as gym
 import numpy as np
@@ -112,3 +112,47 @@ def step(self, action):
     test_env = TestEnv()
     with pytest.raises(AssertionError, match=error_message):
         check_env(env=test_env)
+
+
+class LimitedStepsTestEnv(gym.Env):
+    action_space = spaces.Discrete(n=2)
+    observation_space = spaces.Discrete(n=2)
+
+    def __init__(self, steps_before_termination: int = 1):
+        super().__init__()
+
+        assert steps_before_termination >= 1
+        self._steps_before_termination = steps_before_termination
+
+        self._steps_called = 0
+        self._terminated = False
+
+    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict] = None) -> Tuple[int, Dict]:
+        super().reset(seed=seed)
+
+        self._steps_called = 0
+        self._terminated = False
+
+        return 0, {}
+
+    def step(self, action: np.ndarray) -> Tuple[int, float, bool, bool, Dict[str, Any]]:
+        self._steps_called += 1
+
+        assert not self._terminated
+
+        observation = 0
+        reward = 0.0
+        self._terminated = self._steps_called >= self._steps_before_termination
+        truncated = False
+
+        return observation, reward, self._terminated, truncated, {}
+
+    def render(self) -> None:
+        pass
+
+
+def test_check_env_single_step_env():
+    test_env = LimitedStepsTestEnv(steps_before_termination=1)
+
+    # This should not throw
+    check_env(env=test_env, warn=True)