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https://github.com/vale981/ray
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48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
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"""A very simple contextual bandit example with 3 arms."""
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import argparse
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import random
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import numpy as np
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import gym
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from gym.spaces import Discrete, Box
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from ray import tune
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parser = argparse.ArgumentParser()
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parser.add_argument("--stop-at-reward", type=float, default=10)
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parser.add_argument("--run", type=str, default="contrib/LinUCB")
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class SimpleContextualBandit(gym.Env):
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def __init__(self, config=None):
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self.action_space = Discrete(3)
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self.observation_space = Box(low=-1., high=1., shape=(2, ))
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self.cur_context = None
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def reset(self):
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self.cur_context = random.choice([-1., 1.])
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return np.array([self.cur_context, -self.cur_context])
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def step(self, action):
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rewards_for_context = {
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-1.: [-10, 0, 10],
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1.: [10, 0, -10],
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}
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reward = rewards_for_context[self.cur_context][action]
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return (np.array([-self.cur_context, self.cur_context]), reward, True,
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{
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"regret": 10 - reward
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})
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if __name__ == "__main__":
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args = parser.parse_args()
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tune.run(
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args.run,
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stop={
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"episode_reward_mean": args.stop_at_reward,
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},
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config={
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"env": SimpleContextualBandit,
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})
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