Add support for dict/tuple obs space for VecCheckNaN (#1348)

* Add support for dict/tuple obs space for VecCheckNaN

* Handle list too

* Address comments from code review

* Ignore B028 (explicit stack level)

docs/misc/changelog.rst

@@ -4,7 +4,7 @@ Changelog
 ==========
 
 
-Release 1.8.0a6 (WIP)
+Release 1.8.0a7 (WIP)
 --------------------------
 
 
@@ -18,6 +18,7 @@ New Features:
 ^^^^^^^^^^^^^
 - Added ``repeat_action_probability`` argument in ``AtariWrapper``.
 - Only use ``NoopResetEnv`` and ``MaxAndSkipEnv`` when needed in ``AtariWrapper``
+- Added support for dict/tuple observations spaces for ``VecCheckNan``, the check is now active in the ``env_checker()`` (@DavyMorgan)
 
 `SB3-Contrib`_
 ^^^^^^^^^^^^^^
@@ -1230,4 +1231,4 @@ And all the contributors:
 @Gregwar @ycheng517 @quantitative-technologies @bcollazo @git-thor @TibiGG @cool-RR @MWeltevrede
 @Melanol @qgallouedec @francescoluciano @jlp-ue @burakdmb @timothe-chaumont @honglu2875 @yuanmingqi
 @anand-bala @hughperkins @sidney-tio @AlexPasqua @dominicgkerr @Akhilez @Rocamonde @tobirohrer @ZikangXiong
-@DavyMorgan @luizapozzobon @Bonifatius94 @theSquaredError @harveybellini
+@DavyMorgan @luizapozzobon @Bonifatius94 @theSquaredError @harveybellini @DavyMorgan

setup.cfg

@@ -70,7 +70,8 @@ exclude = (?x)(
 
 [flake8]
 # line breaks before and after binary operators
-ignore = W503,W504,E203,E231
+# ignore explicit stack level
+ignore = W503,W504,E203,E231,B028
 # Ignore import not used when aliases are defined
 per-file-ignores =
   # Default implementation in abstract methods

stable_baselines3/common/env_checker.py

@@ -5,7 +5,7 @@ import gym
 import numpy as np
 from gym import spaces
 
-from stable_baselines3.common.preprocessing import is_image_space_channels_first
+from stable_baselines3.common.preprocessing import check_for_nested_spaces, is_image_space_channels_first
 from stable_baselines3.common.vec_env import DummyVecEnv, VecCheckNan
 
 
@@ -380,6 +380,10 @@ def check_env(env: gym.Env, warn: bool = True, skip_render_check: bool = True) -
     if not skip_render_check:
         _check_render(env, warn=warn)  # pragma: no cover
 
-    # The check only works with numpy arrays
-    if _is_numpy_array_space(observation_space) and _is_numpy_array_space(action_space):
+    try:
+        check_for_nested_spaces(env.observation_space)
+        # The check doesn't support nested observations/dict actions
+        # A warning about it has already been emitted
         _check_nan(env)
+    except NotImplementedError:
+        pass

stable_baselines3/common/vec_env/vec_check_nan.py

@@ -1,6 +1,8 @@
 import warnings
+from typing import List, Tuple
 
 import numpy as np
+from gym import spaces
 
 from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs, VecEnvStepReturn, VecEnvWrapper
 
@@ -26,6 +28,8 @@ class VecCheckNan(VecEnvWrapper):
 
         self._actions: np.ndarray
         self._observations: VecEnvObs
+        if isinstance(venv.action_space, spaces.Dict):
+            raise NotImplementedError("VecCheckNan doesn't support dict action spaces")
 
     def step_async(self, actions: np.ndarray) -> None:
         self._check_val(event="step_async", actions=actions)
@@ -44,19 +48,40 @@ class VecCheckNan(VecEnvWrapper):
         self._observations = observations
         return observations
 
+    def check_array_value(self, name: str, value: np.ndarray) -> List[Tuple[str, str]]:
+        """
+        Check for inf and NaN for a single numpy array.
+
+        :param name: Name of the value being check
+        :param value: Value (numpy array) to check
+        :return: A list of issues found.
+        """
+        found = []
+        has_nan = np.any(np.isnan(value))
+        has_inf = self.check_inf and np.any(np.isinf(value))
+        if has_inf:
+            found.append((name, "inf"))
+        if has_nan:
+            found.append((name, "nan"))
+        return found
+
     def _check_val(self, event: str, **kwargs) -> None:
         # if warn and warn once and have warned once: then stop checking
         if not self.raise_exception and self.warn_once and self._user_warned:
             return
 
         found = []
-        for name, val in kwargs.items():
-            has_nan = np.any(np.isnan(val))
-            has_inf = self.check_inf and np.any(np.isinf(val))
-            if has_inf:
-                found.append((name, "inf"))
-            if has_nan:
-                found.append((name, "nan"))
+        for name, value in kwargs.items():
+            if isinstance(value, (np.ndarray, list)):
+                found += self.check_array_value(name, np.asarray(value))
+            elif isinstance(value, dict):
+                for inner_name, inner_val in value.items():
+                    found += self.check_array_value(f"{name}.{inner_name}", inner_val)
+            elif isinstance(value, tuple):
+                for idx, inner_val in enumerate(value):
+                    found += self.check_array_value(f"{name}.{idx}", inner_val)
+            else:
+                raise TypeError(f"Unsupported observation type {type(value)}.")
 
         if found:
             self._user_warned = True

stable_baselines3/version.txt

@@ -1 +1 @@
-1.8.0a6
+1.8.0a7