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- import argparse
- import os
- import ray
- from ray import air, tune
- from ray.tune.registry import get_trainable_cls
- 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", "torch"],
- default="torch",
- help="The DL framework specifier.",
- )
- parser.add_argument("--stop-iters", type=int, default=200)
- parser.add_argument("--stop-timesteps", type=int, default=100000)
- parser.add_argument("--stop-reward", type=float, default=150.0)
- parser.add_argument(
- "--local-mode",
- action="store_true",
- help="Init Ray in local mode for easier debugging.",
- )
- if __name__ == "__main__":
- args = parser.parse_args()
- ray.init(num_cpus=args.num_cpus or None, local_mode=args.local_mode)
- # Simple generic config.
- config = (
- get_trainable_cls(args.run)
- .get_default_config()
- .environment("CartPole-v1")
- # Run with tracing enabled for tf2.
- .framework(args.framework)
- # Run 3 trials.
- .training(
- lr=tune.grid_search([0.01, 0.001, 0.0001]), train_batch_size=2341
- ) # TEST
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- .resources(num_gpus=int(os.environ.get("RLLIB_NUM_GPUS", "0")))
- )
- stop = {
- "training_iteration": args.stop_iters,
- "timesteps_total": args.stop_timesteps,
- "episode_reward_mean": args.stop_reward,
- }
- # Run tune for some iterations and generate checkpoints.
- tuner = tune.Tuner(
- args.run,
- param_space=config.to_dict(),
- run_config=air.RunConfig(
- stop=stop, checkpoint_config=air.CheckpointConfig(checkpoint_frequency=1)
- ),
- )
- results = tuner.fit()
- # Get the best of the 3 trials by using some metric.
- # NOTE: Choosing the min `episodes_this_iter` automatically picks the trial
- # with the best performance (over the entire run (scope="all")):
- # The fewer episodes, the longer each episode lasted, the more reward we
- # got each episode.
- # Setting scope to "last", "last-5-avg", or "last-10-avg" will only compare
- # (using `mode=min|max`) the average values of the last 1, 5, or 10
- # iterations with each other, respectively.
- # Setting scope to "avg" will compare (using `mode`=min|max) the average
- # values over the entire run.
- metric = "episodes_this_iter"
- # notice here `scope` is `all`, meaning for each trial,
- # all results (not just the last one) will be examined.
- best_result = results.get_best_result(metric=metric, mode="min", scope="all")
- value_best_metric = best_result.metrics_dataframe[metric].min()
- print(
- "Best trial's lowest episode length (over all "
- "iterations): {}".format(value_best_metric)
- )
- # Confirm, we picked the right trial.
- assert value_best_metric <= results.get_dataframe()[metric].min()
- # Get the best checkpoints from the trial, based on different metrics.
- # Checkpoint with the lowest policy loss value:
- if config._enable_learner_api:
- policy_loss_key = "info/learner/default_policy/policy_loss"
- else:
- policy_loss_key = "info/learner/default_policy/learner_stats/policy_loss"
- ckpt = results.get_best_result(metric=policy_loss_key, mode="min").checkpoint
- print("Lowest pol-loss: {}".format(ckpt))
- # Checkpoint with the highest value-function loss:
- if config._enable_learner_api:
- vf_loss_key = "info/learner/default_policy/vf_loss"
- else:
- vf_loss_key = "info/learner/default_policy/learner_stats/vf_loss"
- ckpt = results.get_best_result(metric=vf_loss_key, mode="max").checkpoint
- print("Highest vf-loss: {}".format(ckpt))
- ray.shutdown()
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