Type hint fixes and added __all__ dunder

gymnasium/utils/__init__.py

@@ -8,3 +8,6 @@
 # that verify that our dependencies are actually present.
 from gymnasium.utils.colorize import colorize
 from gymnasium.utils.ezpickle import EzPickle
+
+
+__all__ = ["colorize", "EzPickle"]

gymnasium/vector/async_vector_env.py

@@ -4,9 +4,10 @@
 import time
 from copy import deepcopy
 from enum import Enum
-from typing import Callable, List, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
+from numpy.typing import NDArray
 
 import gymnasium as gym
 from gymnasium import logger
@@ -287,7 +288,7 @@ def step_async(self, actions: np.ndarray):
 
     def step_wait(
         self, timeout: Optional[Union[int, float]] = None
-    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, List[dict]]:
+    ) -> Tuple[Any, NDArray[Any], NDArray[Any], NDArray[Any], dict]:
         """Wait for the calls to :obj:`step` in each sub-environment to finish.
 
         Args:

gymnasium/vector/sync_vector_env.py

@@ -1,8 +1,9 @@
 """A synchronous vector environment."""
 from copy import deepcopy
-from typing import Any, Callable, Iterable, List, Optional, Sequence, Union
+from typing import Any, Callable, Iterable, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
+from numpy.typing import NDArray
 
 from gymnasium import Env
 from gymnasium.spaces import Space
@@ -132,7 +133,7 @@ def step_async(self, actions):
         """Sets :attr:`_actions` for use by the :meth:`step_wait` by converting the ``actions`` to an iterable version."""
         self._actions = iterate(self.action_space, actions)
 
-    def step_wait(self):
+    def step_wait(self) -> Tuple[Any, NDArray[Any], NDArray[Any], NDArray[Any], dict]:
         """Steps through each of the environments returning the batched results.
 
         Returns:

gymnasium/vector/vector_env.py

@@ -2,6 +2,7 @@
 from typing import Any, List, Optional, Tuple, Union
 
 import numpy as np
+from numpy.typing import NDArray
 
 import gymnasium as gym
 from gymnasium.vector.utils.spaces import batch_space
@@ -146,7 +147,7 @@ def step_async(self, actions):
             actions: The actions to take asynchronously
         """
 
-    def step_wait(self, **kwargs):
+    def step_wait(self) -> Tuple[Any, NDArray[Any], NDArray[Any], NDArray[Any], dict]:
         """Retrieves the results of a :meth:`step_async` call.
 
         A call to this method must always be preceded by a call to :meth:`step_async`.
@@ -157,8 +158,11 @@ def step_wait(self, **kwargs):
         Returns:
             The results from the :meth:`step_async` call
         """
+        raise NotImplementedError()
 
-    def step(self, actions):
+    def step(
+        self, actions
+    ) -> Tuple[Any, NDArray[Any], NDArray[Any], NDArray[Any], dict]:
         """Take an action for each parallel environment.
 
         Args:

tests/vector/test_async_vector_env.py

@@ -39,6 +39,7 @@ def test_reset_async_vector_env(shared_memory):
 
     assert isinstance(env.observation_space, Box)
     assert isinstance(observations, np.ndarray)
+    assert isinstance(infos, dict)
     assert observations.dtype == env.observation_space.dtype
     assert observations.shape == (8,) + env.single_observation_space.shape
     assert observations.shape == env.observation_space.shape

tests/vector/test_sync_vector_env.py

@@ -29,6 +29,7 @@ def test_reset_sync_vector_env():
 
     assert isinstance(env.observation_space, Box)
     assert isinstance(observations, np.ndarray)
+    assert isinstance(infos, dict)
     assert observations.dtype == env.observation_space.dtype
     assert observations.shape == (8,) + env.single_observation_space.shape
     assert observations.shape == env.observation_space.shape
@@ -130,15 +131,18 @@ def test_custom_space_sync_vector_env():
 
     assert isinstance(env.single_action_space, CustomSpace)
     assert isinstance(env.action_space, Tuple)
+    assert isinstance(infos, dict)
 
     actions = ("action-2", "action-3", "action-5", "action-7")
-    step_observations, rewards, terminateds, truncateds, _ = env.step(actions)
+    step_observations, rewards, terminateds, truncateds, infos = env.step(actions)
 
     env.close()
 
     assert isinstance(env.single_observation_space, CustomSpace)
     assert isinstance(env.observation_space, Tuple)
 
+    assert isinstance(infos, dict)
+
     assert isinstance(reset_observations, tuple)
     assert reset_observations == ("reset", "reset", "reset", "reset")