modified wrappers

envs/wrappers.py

@@ -34,11 +34,9 @@ class NormalizeActions(gym.Wrapper):
         )
         self._low = np.where(self._mask, env.action_space.low, -1)
         self._high = np.where(self._mask, env.action_space.high, 1)
-
-    def action_space(self):
         low = np.where(self._mask, -np.ones_like(self._low), self._low)
         high = np.where(self._mask, np.ones_like(self._low), self._high)
-        return gym.spaces.Box(low, high, dtype=np.float32)
+        self.action_space = gym.spaces.Box(low, high, dtype=np.float32)
 
     def step(self, action):
         original = (action + 1) / 2 * (self._high - self._low) + self._low
@@ -51,13 +49,10 @@ class OneHotAction(gym.Wrapper):
         assert isinstance(env.action_space, gym.spaces.Discrete)
         super().__init__(env)
         self._random = np.random.RandomState()
-
-    def action_space(self):
         shape = (self.env.action_space.n,)
         space = gym.spaces.Box(low=0, high=1, shape=shape, dtype=np.float32)
-        space.sample = self._sample_action
         space.discrete = True
-        return space
+        self.action_space = space
 
     def step(self, action):
         index = np.argmax(action).astype(int)
@@ -81,25 +76,23 @@ class OneHotAction(gym.Wrapper):
 class RewardObs(gym.Wrapper):
     def __init__(self, env):
         super().__init__(env)
-
-    def observation_space(self):
         spaces = self.env.observation_space.spaces
-        if "reward" not in spaces:
-            spaces["reward"] = gym.spaces.Box(
+        if "obs_reward" not in spaces:
+            spaces["obs_reward"] = gym.spaces.Box(
                 -np.inf, np.inf, shape=(1,), dtype=np.float32
             )
-        return gym.spaces.Dict(spaces)
+        self.observation_space = gym.spaces.Dict(spaces)
 
     def step(self, action):
         obs, reward, done, info = self.env.step(action)
-        if "reward" not in obs:
-            obs["reward"] = reward
+        if "obs_reward" not in obs:
+            obs["obs_reward"] = np.array([reward], dtype=np.float32)
         return obs, reward, done, info
 
     def reset(self):
         obs = self.env.reset()
-        if "reward" not in obs:
-            obs["reward"] = 0.0
+        if "obs_reward" not in obs:
+            obs["obs_reward"] = np.array([0.0], dtype=np.float32)
         return obs