wehub-resource-sync
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
from ray.rllib.algorithms.ppo import PPOConfig
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from ray.rllib.connectors.env_to_module import MeanStdFilter
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from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
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from ray.rllib.examples.envs.classes.multi_agent import MultiAgentPendulum
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from ray.rllib.examples.utils import (
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add_rllib_example_script_args,
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run_rllib_example_script_experiment,
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)
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from ray.rllib.utils.metrics import (
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ENV_RUNNER_RESULTS,
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EPISODE_RETURN_MEAN,
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NUM_ENV_STEPS_SAMPLED_LIFETIME,
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)
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from ray.tune.registry import register_env
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parser = add_rllib_example_script_args(default_timesteps=500000)
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parser.set_defaults(
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num_agents=2,
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)
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# Use `parser` to add your own custom command line options to this script
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# and (if needed) use their values to set up `config` below.
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args = parser.parse_args()
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register_env("multi_agent_pendulum", lambda cfg: MultiAgentPendulum(config=cfg))
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config = (
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PPOConfig()
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.environment("multi_agent_pendulum", env_config={"num_agents": args.num_agents})
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.env_runners(
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env_to_module_connector=lambda env, spaces, device: MeanStdFilter(
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multi_agent=True
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),
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)
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.training(
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train_batch_size_per_learner=1024,
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minibatch_size=128,
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lr=0.0002 * (args.num_learners or 1) ** 0.5,
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gamma=0.95,
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lambda_=0.5,
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)
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.rl_module(
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model_config=DefaultModelConfig(fcnet_activation="relu"),
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)
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.multi_agent(
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policy_mapping_fn=lambda aid, *arg, **kw: f"p{aid}",
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policies={f"p{i}" for i in range(args.num_agents)},
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)
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)
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stop = {
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NUM_ENV_STEPS_SAMPLED_LIFETIME: args.stop_timesteps,
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# Divide by num_agents to get actual return per agent.
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f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": -300.0 * (args.num_agents or 1),
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}
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if __name__ == "__main__":
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run_rllib_example_script_experiment(config, args, stop=stop)
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