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Eric Liang
995ac24a2c
It's possible to configure PPO in a way that ends up discarding most of the samples (they are treated as "stragglers"). Add a warning when this happens, and raise an exception if the waste is particularly egregious.
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["train_batch_size"] = 1000
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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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