mirror of
https://github.com/vale981/ray
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fcf897ee72
update rllib example to use Tuner API. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
import argparse
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import os
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--run", type=str, default="PPO", help="The RLlib-registered algorithm to use."
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)
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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",
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choices=["tf", "tf2", "tfe", "torch"],
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default="tf",
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help="The DL framework specifier.",
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)
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parser.add_argument("--num-workers", type=int, default=0)
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parser.add_argument(
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"--use-n-prev-actions",
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type=int,
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default=0,
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help="How many of the previous actions to use as attention input.",
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)
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parser.add_argument(
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"--use-n-prev-rewards",
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type=int,
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default=0,
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help="How many of the previous rewards to use as attention input.",
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)
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parser.add_argument("--stop-iters", type=int, default=9999)
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parser.add_argument("--stop-timesteps", type=int, default=100000000)
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parser.add_argument("--stop-reward", type=float, default=1000.0)
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if __name__ == "__main__":
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import ray
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from ray import air, 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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config = {
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"env": "VizdoomBasic-v0",
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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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"conv_filters": [],
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"use_attention": True,
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"attention_num_transformer_units": 1,
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"attention_dim": 64,
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"attention_num_heads": 2,
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"attention_memory_inference": 100,
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"attention_memory_training": 50,
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"vf_share_layers": True,
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"attention_use_n_prev_actions": args.use_n_prev_actions,
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"attention_use_n_prev_rewards": args.use_n_prev_rewards,
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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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"num_workers": args.num_workers,
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"vf_loss_coeff": 0.01,
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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.Tuner(
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args.run,
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param_space=config,
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run_config=air.RunConfig(
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stop=stop,
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verbose=2,
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checkpoint_config=air.CheckpointConfig(
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checkpoint_frequency=5,
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checkpoint_at_end=True,
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),
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),
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)
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print(results)
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ray.shutdown()
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