mirror of
https://github.com/vale981/ray
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41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
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from copy import deepcopy
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import gym
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import numpy as np
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from gym.spaces import Discrete, Dict, Box
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class CartPole:
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"""
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Wrapper for gym CartPole environment where the reward
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is accumulated to the end
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"""
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def __init__(self, config=None):
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self.env = gym.make("CartPole-v0")
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self.action_space = Discrete(2)
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self.observation_space = Dict({
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"obs": self.env.observation_space,
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"action_mask": Box(low=0, high=1, shape=(self.action_space.n, ))
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})
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self.running_reward = 0
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def reset(self):
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self.running_reward = 0
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return {"obs": self.env.reset(), "action_mask": np.array([1, 1])}
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def step(self, action):
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obs, rew, done, info = self.env.step(action)
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self.running_reward += rew
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score = self.running_reward if done else 0
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return {"obs": obs, "action_mask": np.array([1, 1])}, score, done, info
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def set_state(self, state):
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self.running_reward = state[1]
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self.env = deepcopy(state[0])
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obs = np.array(list(self.env.unwrapped.state))
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return {"obs": obs, "action_mask": np.array([1, 1])}
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def get_state(self):
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return deepcopy(self.env), self.running_reward
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