Fix: TimeAwareObservation did not support float64 and tried to forbidden access private attribute.

fancy_gym/utils/time_aware_observation.py

@@ -1,20 +1,65 @@
+from gymnasium.spaces import Box
 import gymnasium as gym
 import numpy as np
 
 
-class TimeAwareObservation(gym.wrappers.TimeAwareObservation):
+class TimeAwareObservation(gym.ObservationWrapper, gym.utils.RecordConstructorArgs):
+    """Augment the observation with the current time step in the episode.
 
-    def __init__(self, env: gym.Env):
-        super().__init__(env)
-        self._max_episode_steps = env.spec.max_episode_steps
+    The observation space of the wrapped environment is assumed to be a flat :class:`Box`.
+    In particular, pixel observations are not supported. This wrapper will append the current timestep within the current episode to the observation.
+    The timestep will be indicated as a number between 0 and 1.
+    """
+
+    def __init__(self, env: gym.Env, enforce_dtype_float32=False):
+        """Initialize :class:`TimeAwareObservation` that requires an environment with a flat :class:`Box` observation space.
+
+        Args:
+            env: The environment to apply the wrapper
+        """
+        gym.utils.RecordConstructorArgs.__init__(self)
+        gym.ObservationWrapper.__init__(self, env)
+        assert isinstance(env.observation_space, Box)
+        if enforce_dtype_float32:
+            assert env.observation_space.dtype == np.float32,
+            'TimeAwareObservation was given an environment with a dtype!=np.float32 ('+str(env.observation_space.dtype)+'). This requirement can be removed by setting enforce_dtype_float32=False.'
+        dtype = env.observation_space.dtype
+        low = np.append(self.observation_space.low, 0.0)
+        high = np.append(self.observation_space.high, np.inf)
+        self.observation_space = Box(low, high, dtype=dtype)
+        self.is_vector_env = getattr(env, "is_vector_env", False)
 
     def observation(self, observation):
-        """Adds to the observation with the current time step normalized with max steps.
+        """Adds to the observation with the current time step.
 
         Args:
             observation: The observation to add the time step to
 
         Returns:
-            The observation with the time step appended to
+            The observation with the time step appended to (relative to total number of steps)
         """
-        return np.append(observation, self.t / self._max_episode_steps)
+        return np.append(observation, self.t / getattr(self.env, '_max_episode_steps')
+
+    def step(self, action):
+        """Steps through the environment, incrementing the time step.
+
+        Args:
+            action: The action to take
+
+        Returns:
+            The environment's step using the action.
+        """
+        self.t += 1
+        return super().step(action)
+
+    def reset(self, **kwargs):
+        """Reset the environment setting the time to zero.
+
+        Args:
+            **kwargs: Kwargs to apply to env.reset()
+
+        Returns:
+            The reset environment
+        """
+        self.t=0
+        return super().reset(**kwargs)