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https://github.com/vale981/ray
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026bf01071
* Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 * Reformatting * Fixing tests * Move atari-py install conditional to req.txt * migrate to new ale install method * Fix QMix, SAC, and MADDPA too. * Unpin gym and deprecate pendulum v0 Many tests in rllib depended on pendulum v0, however in gym 0.21, pendulum v0 was deprecated in favor of pendulum v1. This may change reward thresholds, so will have to potentially rerun all of the pendulum v1 benchmarks, or use another environment in favor. The same applies to frozen lake v0 and frozen lake v1 Lastly, all of the RLlib tests and have been moved to python 3.7 * Add gym installation based on python version. Pin python<= 3.6 to gym 0.19 due to install issues with atari roms in gym 0.20 Move atari-py install conditional to req.txt migrate to new ale install method Make parametric_actions_cartpole return float32 actions/obs Adding type conversions if obs/actions don't match space Add utils to make elements match gym space dtypes Co-authored-by: Jun Gong <jungong@anyscale.com> Co-authored-by: sven1977 <svenmika1977@gmail.com>
46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
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 {
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"obs": self.env.reset(),
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"action_mask": np.array([1, 1], dtype=np.float32)
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}
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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 {
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"obs": obs,
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"action_mask": np.array([1, 1], dtype=np.float32)
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}, 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], dtype=np.float32)}
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def get_state(self):
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return deepcopy(self.env), self.running_reward
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