mirror of
https://github.com/vale981/ray
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72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
"""Example of running a custom hand-coded policy alongside trainable policies.
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This example has two policies:
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(1) a simple PG policy
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(2) a hand-coded policy that acts at random in the env (doesn't learn)
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In the console output, you can see the PG policy does much better than random:
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Result for PG_multi_cartpole_0:
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...
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policy_reward_mean:
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pg_policy: 185.23
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random: 21.255
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...
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"""
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import argparse
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import gym
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import ray
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from ray import tune
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from ray.rllib.policy import Policy
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from ray.rllib.tests.test_multi_agent_env import MultiCartpole
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from ray.tune.registry import register_env
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parser = argparse.ArgumentParser()
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parser.add_argument("--num-iters", type=int, default=20)
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class RandomPolicy(Policy):
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"""Hand-coded policy that returns random actions."""
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def compute_actions(self,
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obs_batch,
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state_batches=None,
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prev_action_batch=None,
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prev_reward_batch=None,
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info_batch=None,
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episodes=None,
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**kwargs):
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"""Compute actions on a batch of observations."""
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return [self.action_space.sample() for _ in obs_batch], [], {}
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def learn_on_batch(self, samples):
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"""No learning."""
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return {}
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init()
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# Simple environment with 4 independent cartpole entities
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register_env("multi_cartpole", lambda _: MultiCartpole(4))
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single_env = gym.make("CartPole-v0")
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obs_space = single_env.observation_space
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act_space = single_env.action_space
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tune.run(
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"PG",
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stop={"training_iteration": args.num_iters},
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config={
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"env": "multi_cartpole",
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"multiagent": {
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"policies": {
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"pg_policy": (None, obs_space, act_space, {}),
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"random": (RandomPolicy, obs_space, act_space, {}),
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},
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"policy_mapping_fn": (
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lambda agent_id: ["pg_policy", "random"][agent_id % 2]),
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},
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},
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)
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