mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
42991d723f
Cleans up of the rllib/examples folder by moving all example Envs into rllibexamples/env (so they can be used by other scripts and tests as well).
45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
import argparse
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from ray.rllib.examples.env.stateless_cartpole import StatelessCartPole
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parser = argparse.ArgumentParser()
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parser.add_argument("--stop", type=int, default=200)
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parser.add_argument("--torch", action="store_true")
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parser.add_argument("--use-prev-action-reward", action="store_true")
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parser.add_argument("--run", type=str, default="PPO")
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parser.add_argument("--num-cpus", type=int, default=0)
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if __name__ == "__main__":
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import ray
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from ray import tune
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args = parser.parse_args()
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ray.init(num_cpus=args.num_cpus or None)
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configs = {
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"PPO": {
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"num_sgd_iter": 5,
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"vf_share_layers": True,
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"vf_loss_coeff": 0.0001,
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},
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"IMPALA": {
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"num_workers": 2,
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"num_gpus": 0,
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"vf_loss_coeff": 0.01,
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},
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}
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tune.run(
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args.run,
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stop={"episode_reward_mean": args.stop},
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config=dict(
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configs[args.run], **{
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"env": StatelessCartPole,
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"model": {
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"use_lstm": True,
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"lstm_use_prev_action_reward": args.use_prev_action_reward,
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},
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"use_pytorch": args.torch,
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}),
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)
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