wehub-resource-sync
56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
import gymnasium as gym
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from gymnasium.wrappers import TimeLimit
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from ray.rllib.algorithms.ppo import PPOConfig
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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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EVALUATION_RESULTS,
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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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# 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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# For training, use a time-truncated (max. 50 timestep) version of CartPole-v1.
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register_env(
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"cartpole_truncated",
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lambda _: TimeLimit(gym.make("CartPole-v1"), max_episode_steps=50),
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)
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config = (
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PPOConfig()
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.environment("cartpole_truncated")
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.env_runners(num_envs_per_env_runner=10)
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.training(
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lr=0.0003,
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num_epochs=6,
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vf_loss_coeff=0.01,
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)
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# For evaluation, use the "real" CartPole-v1 env (up to 500 steps).
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.evaluation(
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evaluation_config=PPOConfig.overrides(
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env="CartPole-v1",
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explore=False,
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),
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evaluation_interval=1,
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evaluation_num_env_runners=1,
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)
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)
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stop = {
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f"{NUM_ENV_STEPS_SAMPLED_LIFETIME}": 500000,
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f"{EVALUATION_RESULTS}/{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": 80.0,
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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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