mirror of
https://github.com/wassname/ray.git
synced 2026-07-05 07:56:05 +08:00
Eric Liang
60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
""" Multiagent mountain car. Each agent outputs an action which
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is summed to form the total action. This is a discrete
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multiagent example
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"""
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import gym
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from gym.envs.registration import register
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import ray
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import ray.rllib.agents.ppo as ppo
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from ray.tune.registry import register_env
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env_name = "MultiAgentMountainCarEnv"
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env_version_num = 0
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env_name = env_name + '-v' + str(env_version_num)
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def pass_params_to_gym(env_name):
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global env_version_num
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register(
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id=env_name,
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entry_point=(
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"ray.rllib.examples.legacy_multiagent.multiagent_mountaincar_env:"
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"MultiAgentMountainCarEnv"),
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max_episode_steps=200,
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kwargs={})
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def create_env(env_config):
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pass_params_to_gym(env_name)
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env = gym.envs.make(env_name)
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return env
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if __name__ == '__main__':
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register_env(env_name, lambda env_config: create_env(env_config))
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config = ppo.DEFAULT_CONFIG.copy()
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horizon = 10
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num_cpus = 4
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ray.init(num_cpus=num_cpus, redirect_output=True)
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config["num_workers"] = num_cpus
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config["timesteps_per_batch"] = 10
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config["num_sgd_iter"] = 10
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config["gamma"] = 0.999
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config["horizon"] = horizon
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config["use_gae"] = False
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config["model"].update({"fcnet_hiddens": [256, 256]})
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options = {
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"multiagent_obs_shapes": [2, 2],
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"multiagent_act_shapes": [1, 1],
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"multiagent_shared_model": False,
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"multiagent_fcnet_hiddens": [[32, 32]] * 2
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}
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config["model"].update({"custom_options": options})
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alg = ppo.PPOAgent(env=env_name, config=config)
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for i in range(1):
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alg.train()
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