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https://github.com/vale981/ray
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57544b1ff9
- This PR completes any previously missing PyTorch Model counterparts to TFModels in examples/models. - It also makes sure, all example scripts in the rllib/examples folder are tested for both frameworks and learn the given task (this is often currently not checked) using a --as-test flag in connection with a --stop-reward.
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
"""A very simple contextual bandit example with 3 arms."""
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import argparse
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import gym
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from gym.spaces import Discrete, Box
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import numpy as np
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import random
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from ray import tune
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from ray.rllib.utils.test_utils import check_learning_achieved
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parser = argparse.ArgumentParser()
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parser.add_argument("--run", type=str, default="contrib/LinUCB")
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parser.add_argument("--as-test", action="store_true")
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parser.add_argument("--stop-iters", type=int, default=200)
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parser.add_argument("--stop-timesteps", type=int, default=100000)
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parser.add_argument("--stop-reward", type=float, default=10.0)
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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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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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"episode_reward_mean": args.stop_reward,
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}
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config = {
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"env": SimpleContextualBandit,
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}
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results = tune.run(args.run, config=config, stop=stop)
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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