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https://github.com/vale981/ray
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65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
import gym
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from gym.spaces import Discrete, Tuple
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import numpy as np
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from ray.rllib.examples.env.multi_agent import make_multi_agent
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class RandomEnv(gym.Env):
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"""A randomly acting environment.
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Can be instantiated with arbitrary action-, observation-, and reward
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spaces. Observations and rewards are generated by simply sampling from the
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observation/reward spaces. The probability of a `done=True` can be
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configured as well.
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"""
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def __init__(self, config):
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# Action space.
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self.action_space = config.get("action_space", Discrete(2))
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# Observation space from which to sample.
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self.observation_space = config.get("observation_space", Discrete(2))
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# Reward space from which to sample.
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self.reward_space = config.get(
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"reward_space",
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gym.spaces.Box(low=-1.0, high=1.0, shape=(), dtype=np.float32))
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# Chance that an episode ends at any step.
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self.p_done = config.get("p_done", 0.1)
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# A max episode length.
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self.max_episode_len = config.get("max_episode_len", None)
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# Whether to check action bounds.
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self.check_action_bounds = config.get("check_action_bounds", False)
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# Steps taken so far (after last reset).
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self.steps = 0
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def reset(self):
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self.steps = 0
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return self.observation_space.sample()
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def step(self, action):
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if self.check_action_bounds and not self.action_space.contains(action):
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raise ValueError("Illegal action for {}: {}".format(
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self.action_space, action))
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if (isinstance(self.action_space, Tuple)
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and len(action) != len(self.action_space.spaces)):
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raise ValueError("Illegal action for {}: {}".format(
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self.action_space, action))
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self.steps += 1
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done = False
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# We are done as per our max-episode-len.
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if self.max_episode_len is not None and \
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self.steps >= self.max_episode_len:
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done = True
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# Max not reached yet -> Sample done via p_done.
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elif self.p_done > 0.0:
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done = bool(
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np.random.choice(
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[True, False], p=[self.p_done, 1.0 - self.p_done]))
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return self.observation_space.sample(), \
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float(self.reward_space.sample()), done, {}
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# Multi-agent version of the RandomEnv.
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RandomMultiAgentEnv = make_multi_agent(lambda c: RandomEnv(c))
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