mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
import argparse
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import os
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from ray.rllib.examples.env.stateless_cartpole import StatelessCartPole
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from ray.rllib.utils.test_utils import check_learning_achieved
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parser = argparse.ArgumentParser()
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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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parser.add_argument(
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"--framework", choices=["tf2", "tf", "tfe", "torch"], default="tf")
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parser.add_argument("--as-test", action="store_true")
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parser.add_argument("--use-prev-action", action="store_true")
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parser.add_argument("--use-prev-reward", action="store_true")
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parser.add_argument("--stop-iters", type=int, default=200)
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parser.add_argument("--stop-timesteps", type=int, default=100000)
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parser.add_argument("--stop-reward", type=float, default=150.0)
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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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"model": {
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"vf_share_layers": True,
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},
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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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config = dict(
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configs[args.run],
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**{
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"env": StatelessCartPole,
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"model": {
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"use_lstm": True,
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"lstm_use_prev_action": args.use_prev_action,
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"lstm_use_prev_reward": args.use_prev_reward,
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},
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"framework": args.framework,
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# Run with tracing enabled for tfe/tf2.
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"eager_tracing": args.framework in ["tfe", "tf2"],
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})
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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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"episode_reward_mean": args.stop_reward,
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}
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results = tune.run(args.run, config=config, stop=stop, verbose=2)
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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