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- import argparse
- import os
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--run",
- type=str,
- default="PPO",
- help="The RLlib-registered algorithm to use.")
- parser.add_argument("--num-cpus", type=int, default=0)
- parser.add_argument(
- "--framework",
- choices=["tf", "tf2", "tfe", "torch"],
- default="tf",
- help="The DL framework specifier.")
- parser.add_argument(
- "--from-checkpoint",
- type=str,
- default=None,
- help="Full path to a checkpoint file for restoring a previously saved "
- "Trainer state.")
- parser.add_argument("--num-workers", type=int, default=0)
- parser.add_argument(
- "--use-n-prev-actions",
- type=int,
- default=0,
- help="How many of the previous actions to use as attention input.")
- parser.add_argument(
- "--use-n-prev-rewards",
- type=int,
- default=0,
- help="How many of the previous rewards to use as attention input.")
- parser.add_argument("--stop-iters", type=int, default=9999)
- parser.add_argument("--stop-timesteps", type=int, default=100000000)
- parser.add_argument("--stop-reward", type=float, default=1000.0)
- if __name__ == "__main__":
- import ray
- from ray import tune
- args = parser.parse_args()
- ray.init(num_cpus=args.num_cpus or None)
- config = {
- "env": "VizdoomBasic-v0",
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
- "model": {
- "conv_filters": [],
- "use_attention": True,
- "attention_num_transformer_units": 1,
- "attention_dim": 64,
- "attention_num_heads": 2,
- "attention_memory_inference": 100,
- "attention_memory_training": 50,
- "vf_share_layers": True,
- "attention_use_n_prev_actions": args.use_n_prev_actions,
- "attention_use_n_prev_rewards": args.use_n_prev_rewards,
- },
- "framework": args.framework,
- # Run with tracing enabled for tfe/tf2.
- "eager_tracing": args.framework in ["tfe", "tf2"],
- "num_workers": args.num_workers,
- "vf_loss_coeff": 0.01,
- }
- stop = {
- "training_iteration": args.stop_iters,
- "timesteps_total": args.stop_timesteps,
- "episode_reward_mean": args.stop_reward,
- }
- results = tune.run(
- args.run,
- config=config,
- stop=stop,
- verbose=2,
- checkpoint_freq=5,
- checkpoint_at_end=True,
- restore=args.from_checkpoint,
- )
- print(results)
- ray.shutdown()
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