Forward environment properties to the wrapper (openai#2373)

* Forward environment properties to the wrapper, fixes openai#2175

* Add tests for property forwarding in Wrapper

* Rename klass to class_ in test_core

gym/core.py

@@ -224,10 +224,11 @@ class Wrapper(Env):
 
     def __init__(self, env):
         self.env = env
-        self.action_space = self.env.action_space
-        self.observation_space = self.env.observation_space
-        self.reward_range = self.env.reward_range
-        self.metadata = self.env.metadata
+
+        self._action_space = None
+        self._observation_space = None
+        self._reward_range = None
+        self._metadata = None
 
     def __getattr__(self, name):
         if name.startswith("_"):
@@ -244,6 +245,46 @@ def spec(self):
     def class_name(cls):
         return cls.__name__
 
+    @property
+    def action_space(self):
+        if self._action_space is None:
+            return self.env.action_space
+        return self._action_space
+
+    @action_space.setter
+    def action_space(self, space):
+        self._action_space = space
+
+    @property
+    def observation_space(self):
+        if self._observation_space is None:
+            return self.env.observation_space
+        return self._observation_space
+
+    @observation_space.setter
+    def observation_space(self, space):
+        self._observation_space = space
+
+    @property
+    def reward_range(self):
+        if self._reward_range is None:
+            return self.env.reward_range
+        return self._reward_range
+
+    @reward_range.setter
+    def reward_range(self, value):
+        self._reward_range = value
+
+    @property
+    def metadata(self):
+        if self._metadata is None:
+            return self.env.metadata
+        return self._metadata
+
+    @metadata.setter
+    def metadata(self, value):
+        self._metadata = value
+
     def step(self, action):
         return self.env.step(action)
 

gym/tests/test_core.py

@@ -1,4 +1,7 @@
-from gym import core
+import pytest
+import numpy as np
+
+from gym import core, spaces
 
 
 class ArgumentEnv(core.Env):
@@ -9,9 +12,99 @@ def __init__(self, arg):
         self.arg = arg
 
 
+class UnittestEnv(core.Env):
+    observation_space = spaces.Box(low=0, high=255, shape=(64, 64, 3), dtype=np.uint8)
+    action_space = spaces.Discrete(3)
+
+    def reset(self):
+        return self.observation_space.sample()  # Dummy observation
+
+    def step(self, action):
+        observation = self.observation_space.sample()  # Dummy observation
+        return (observation, 0.0, False, {})
+
+
+class UnknownSpacesEnv(core.Env):
+    """This environment defines its observation & action spaces only
+    after the first call to reset. Although this pattern is sometimes
+    necessary when implementing a new environment (e.g. if it depends
+    on external resources), it is not encouraged.
+    """
+
+    def reset(self):
+        self.observation_space = spaces.Box(
+            low=0, high=255, shape=(64, 64, 3), dtype=np.uint8
+        )
+        self.action_space = spaces.Discrete(3)
+        return self.observation_space.sample()  # Dummy observation
+
+    def step(self, action):
+        observation = self.observation_space.sample()  # Dummy observation
+        return (observation, 0.0, False, {})
+
+
+class NewPropertyWrapper(core.Wrapper):
+    def __init__(
+        self,
+        env,
+        observation_space=None,
+        action_space=None,
+        reward_range=None,
+        metadata=None,
+    ):
+        super().__init__(env)
+        if observation_space is not None:
+            # Only set the observation space if not None to test property forwarding
+            self.observation_space = observation_space
+        if action_space is not None:
+            self.action_space = action_space
+        if reward_range is not None:
+            self.reward_range = reward_range
+        if metadata is not None:
+            self.metadata = metadata
+
+
 def test_env_instantiation():
     # This looks like a pretty trivial, but given our usage of
     # __new__, it's worth having.
     env = ArgumentEnv("arg")
     assert env.arg == "arg"
     assert env.calls == 1
+
+
+properties = [
+    {
+        "observation_space": spaces.Box(
+            low=0.0, high=1.0, shape=(64, 64, 3), dtype=np.float32
+        )
+    },
+    {"action_space": spaces.Discrete(2)},
+    {"reward_range": (-1.0, 1.0)},
+    {"metadata": {"render.modes": ["human", "rgb_array"]}},
+    {
+        "observation_space": spaces.Box(
+            low=0.0, high=1.0, shape=(64, 64, 3), dtype=np.float32
+        ),
+        "action_space": spaces.Discrete(2),
+    },
+]
+
+
+@pytest.mark.parametrize("class_", [UnittestEnv, UnknownSpacesEnv])
+@pytest.mark.parametrize("props", properties)
+def test_wrapper_property_forwarding(class_, props):
+    env = class_()
+    env = NewPropertyWrapper(env, **props)
+
+    # If UnknownSpacesEnv, then call reset to define the spaces
+    if isinstance(env.unwrapped, UnknownSpacesEnv):
+        _ = env.reset()
+
+    # Test the properties set by the wrapper
+    for key, value in props.items():
+        assert getattr(env, key) == value
+
+    # Otherwise, test if the properties are forwarded
+    all_properties = {"observation_space", "action_space", "reward_range", "metadata"}
+    for key in all_properties - props.keys():
+        assert getattr(env, key) == getattr(env.unwrapped, key)