Remove old file

fancy_gym/utils/time_aware_observation.py

@@ -1,79 +0,0 @@
-from gymnasium.spaces import Box, Dict
-from gym.spaces import Box as OldBox
-import gymnasium as gym
-import numpy as np
-import copy
-
-
-class TimeAwareObservation(gym.ObservationWrapper, gym.utils.RecordConstructorArgs):
-    """Augment the observation with the current time step in the episode.
-
-    The observation space of the wrapped environment is assumed to be a flat :class:`Box` or flattable :class:`Dict`.
-    In particular, pixel observations are not supported. This wrapper will append the current progress within the current episode to the observation.
-    The progress 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` or flattable :class:`Dict` observation space.
-
-        Args:
-            env: The environment to apply the wrapper
-        """
-        gym.utils.RecordConstructorArgs.__init__(self)
-        gym.ObservationWrapper.__init__(self, env)
-        allowed_classes = [Box, OldBox, Dict]
-        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.'
-        assert env.observation_space.__class__ in allowed_classes, str(env.observation_space)+' is not supported. Only Box or Dict'
-
-        if env.observation_space.__class__ in [Box, OldBox]:
-            dtype = env.observation_space.dtype
-
-            low = np.append(env.observation_space.low, 0.0)
-            high = np.append(env.observation_space.high, 1.0)
-
-            self.observation_space = Box(low, high, dtype=dtype)
-        else:
-            spaces = copy.copy(env.observation_space.spaces)
-            dtype = np.float64
-            spaces['time_awareness'] = Box(0, 1, dtype=dtype)
-
-            self.observation_space = Dict(spaces)
-
-        self.is_vector_env = getattr(env, "is_vector_env", False)
-
-    def observation(self, observation):
-        """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 (relative to total number of steps)
-        """
-        return np.append(observation, self.t / self.env.spec.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)