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https://github.com/vale981/ray
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46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
import gym
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from gym.spaces import Box, Discrete
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import numpy as np
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class RepeatAfterMeEnv(gym.Env):
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"""Env in which the observation at timestep minus n must be repeated."""
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def __init__(self, config=None):
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config = config or {}
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if config.get("continuous"):
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self.observation_space = Box(-1.0, 1.0, (2, ))
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else:
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self.observation_space = Discrete(2)
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self.action_space = self.observation_space
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# Note: Set `repeat_delay` to 0 for simply repeating the seen
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# observation (no delay).
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self.delay = config.get("repeat_delay", 1)
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self.episode_len = config.get("episode_len", 100)
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self.history = []
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def reset(self):
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self.history = [0] * self.delay
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return self._next_obs()
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def step(self, action):
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obs = self.history[-(1 + self.delay)]
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# Box: -abs(diff).
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if isinstance(self.action_space, Box):
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reward = -np.sum(np.abs(action - obs))
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# Discrete: +1.0 if exact match, -1.0 otherwise.
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if isinstance(self.action_space, Discrete):
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reward = 1.0 if action == obs else -1.0
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done = len(self.history) > self.episode_len
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return self._next_obs(), reward, done, {}
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def _next_obs(self):
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if isinstance(self.observation_space, Box):
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token = np.random.random(size=(2, ))
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else:
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token = np.random.choice([0, 1])
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self.history.append(token)
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return token
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