wehub-resource-sync
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
from torch import nn
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from ray.rllib.algorithms.sac import SACConfig
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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(
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default_timesteps=500000,
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)
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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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SACConfig()
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.environment("multi_agent_pendulum", env_config={"num_agents": args.num_agents})
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.training(
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initial_alpha=1.001,
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# Use a smaller learning rate for the policy.
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actor_lr=2e-4 * (args.num_learners or 1) ** 0.5,
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critic_lr=8e-4 * (args.num_learners or 1) ** 0.5,
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alpha_lr=9e-4 * (args.num_learners or 1) ** 0.5,
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lr=None,
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target_entropy="auto",
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n_step=(2, 5),
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tau=0.005,
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train_batch_size_per_learner=256,
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target_network_update_freq=1,
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replay_buffer_config={
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"type": "MultiAgentPrioritizedEpisodeReplayBuffer",
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"capacity": 100000,
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"alpha": 1.0,
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"beta": 0.0,
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},
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num_steps_sampled_before_learning_starts=256,
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)
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.rl_module(
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model_config=DefaultModelConfig(
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fcnet_hiddens=[256, 256],
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fcnet_activation="relu",
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fcnet_kernel_initializer=nn.init.xavier_uniform_,
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head_fcnet_hiddens=[],
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head_fcnet_activation=None,
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head_fcnet_kernel_initializer=nn.init.orthogonal_,
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head_fcnet_kernel_initializer_kwargs={"gain": 0.01},
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fusionnet_hiddens=[256, 256, 256],
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fusionnet_activation="relu",
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),
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)
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.reporting(
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metrics_num_episodes_for_smoothing=5,
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)
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)
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if args.num_agents > 0:
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config.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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stop = {
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NUM_ENV_STEPS_SAMPLED_LIFETIME: args.stop_timesteps,
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# `episode_return_mean` is the sum of all agents/policies' returns.
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f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": -450.0 * args.num_agents,
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}
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if __name__ == "__main__":
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assert (
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args.num_agents > 0
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), "The `--num-agents` arg must be > 0 for this script to work."
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run_rllib_example_script_experiment(config, args, stop=stop)
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