"""Wrapper for adding time aware observations to environment observation."""
import numpy as np
import gymnasium as gym
from gymnasium.spaces import Box
[docs]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`.
In particular, pixel observations are not supported. This wrapper will append the current timestep within the current episode to the observation.
Example:
>>> import gymnasium as gym
>>> from gymnasium.wrappers import TimeAwareObservation
>>> env = gym.make("CartPole-v1")
>>> env = TimeAwareObservation(env)
>>> env.reset(seed=42)
(array([ 0.0273956 , -0.00611216, 0.03585979, 0.0197368 , 0. ]), {})
>>> _ = env.action_space.seed(42)
>>> env.step(env.action_space.sample())[0]
array([ 0.02727336, -0.20172954, 0.03625453, 0.32351476, 1. ])
"""
def __init__(self, env: gym.Env):
"""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)
assert env.observation_space.dtype == np.float32
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=np.float32)
try:
self.is_vector_env = self.get_wrapper_attr("is_vector_env")
except AttributeError:
self.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
"""
return np.append(observation, self.t)
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)
