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Dominik Moritz Roth 2023-06-18 14:25:59 +02:00
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fancy_gym/utils/time_aware_observation.py
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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)