79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
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"""
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Adapted from: https://github.com/openai/gym/blob/907b1b20dd9ac0cba5803225059b9c6673702467/gym/wrappers/time_aware_observation.py
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License: MIT
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Copyright (c) 2016 OpenAI (https://openai.com)
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Wrapper for adding time aware observations to environment observation.
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"""
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import numpy as np
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import gym
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from gym.spaces import Box
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class TimeAwareObservation(gym.ObservationWrapper):
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"""Augment the observation with the current time step in the episode.
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The observation space of the wrapped environment is assumed to be a flat :class:`Box`.
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In particular, pixel observations are not supported. This wrapper will append the current timestep
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within the current episode to the observation.
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Example:
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>>> import gym
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>>> env = gym.make('CartPole-v1')
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>>> env = TimeAwareObservation(env)
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>>> env.reset()
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array([ 0.03810719, 0.03522411, 0.02231044, -0.01088205, 0. ])
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>>> env.step(env.action_space.sample())[0]
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array([ 0.03881167, -0.16021058, 0.0220928 , 0.28875574, 1. ])
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"""
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def __init__(self, env: gym.Env):
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"""Initialize :class:`TimeAwareObservation` that requires an environment with a flat :class:`Box`
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observation space.
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Args:
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env: The environment to apply the wrapper
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"""
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super().__init__(env)
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assert isinstance(env.observation_space, Box)
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low = np.append(self.observation_space.low, 0.0)
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high = np.append(self.observation_space.high, np.inf)
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self.observation_space = Box(low, high, dtype=self.observation_space.dtype)
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self.t = 0
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def observation(self, observation):
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"""Adds to the observation with the current time step.
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Args:
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observation: The observation to add the time step to
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Returns:
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The observation with the time step appended to
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"""
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return np.append(observation, self.t)
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def step(self, action):
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"""Steps through the environment, incrementing the time step.
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Args:
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action: The action to take
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Returns:
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The environment's step using the action.
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"""
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self.t += 1
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return super().step(action)
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def reset(self, **kwargs):
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"""Reset the environment setting the time to zero.
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Args:
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**kwargs: Kwargs to apply to env.reset()
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Returns:
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The reset environment
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"""
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self.t = 0
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return super().reset(**kwargs)
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