mirror of
https://github.com/vale981/ray
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33 lines
988 B
Python
33 lines
988 B
Python
import gym
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from gym.spaces import Discrete, Tuple
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import random
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class CorrelatedActionsEnv(gym.Env):
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"""Simple env in which the policy has to emit a tuple of equal actions.
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The best score would be ~200 reward."""
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def __init__(self, _):
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self.observation_space = Discrete(2)
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self.action_space = Tuple([Discrete(2), Discrete(2)])
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self.last_observation = None
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def reset(self):
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self.t = 0
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self.last_observation = random.choice([0, 1])
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return self.last_observation
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def step(self, action):
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self.t += 1
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a1, a2 = action
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reward = 0
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# Encourage correlation between most recent observation and a1.
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if a1 == self.last_observation:
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reward += 5
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# Encourage correlation between a1 and a2.
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if a1 == a2:
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reward += 5
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done = self.t > 20
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self.last_observation = random.choice([0, 1])
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return self.last_observation, reward, done, {}
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