mirror of
https://github.com/vale981/ray
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123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
import argparse
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import os
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from ray.rllib.agents.callbacks import DefaultCallbacks
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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(
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"--evaluation-num-episodes",
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type=lambda v: v if v == "auto" else int(v),
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default=13,
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help="Number of evaluation episodes to run each iteration. "
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"If 'auto', will run as many as possible during train pass.")
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parser.add_argument(
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"--run",
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type=str,
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default="PPO",
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help="The RLlib-registered algorithm to use.")
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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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parser.add_argument(
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"--as-test",
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action="store_true",
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help="Whether this script should be run as a test: --stop-reward must "
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"be achieved within --stop-timesteps AND --stop-iters.")
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parser.add_argument(
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"--stop-iters",
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type=int,
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default=200,
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help="Number of iterations to train.")
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parser.add_argument(
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"--stop-timesteps",
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type=int,
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default=200000,
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help="Number of timesteps to train.")
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parser.add_argument(
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"--stop-reward",
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type=float,
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default=180.0,
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help="Reward at which we stop training.")
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parser.add_argument(
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"--local-mode",
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action="store_true",
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help="Init Ray in local mode for easier debugging.")
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class AssertNumEvalEpisodesCallback(DefaultCallbacks):
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def on_train_result(self, *, trainer, result, **kwargs):
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# Make sure we always run exactly n evaluation episodes,
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# no matter what the other settings are (such as
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# `evaluation_num_workers` or `evaluation_parallel_to_training`).
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if "evaluation" in result:
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hist_stats = result["evaluation"]["hist_stats"]
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num_episodes_done = len(hist_stats["episode_lengths"])
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# Compare number of entries in episode_lengths (this is the
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# number of episodes actually run) with desired number of
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# episodes from the config.
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if isinstance(trainer.config["evaluation_num_episodes"], int):
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assert num_episodes_done == \
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trainer.config["evaluation_num_episodes"]
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else:
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assert trainer.config["evaluation_num_episodes"] == "auto"
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assert num_episodes_done >= \
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trainer.config["evaluation_num_workers"]
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print("Number of run evaluation episodes: "
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f"{num_episodes_done} (ok)!")
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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, local_mode=args.local_mode)
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config = {
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"env": "CartPole-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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"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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# Parallel evaluation+training config.
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# Switch on evaluation in parallel with training.
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"evaluation_parallel_to_training": True,
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# Use two evaluation workers. Must be >0, otherwise,
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# evaluation will run on a local worker and block (no parallelism).
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"evaluation_num_workers": 2,
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# Evaluate every other training iteration (together
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# with every other call to Trainer.train()).
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"evaluation_interval": 2,
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# Run for n episodes (properly distribute load amongst all eval
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# workers). The longer it takes to evaluate, the more
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# sense it makes to use `evaluation_parallel_to_training=True`.
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# Use "auto" to run evaluation for roughly as long as the training
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# step takes.
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"evaluation_num_episodes": args.evaluation_num_episodes,
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# Use a custom callback that asserts that we are running the
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# configured exact number of episodes per evaluation OR - in auto
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# mode - run at least as many episodes as we have eval workers.
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"callbacks": AssertNumEvalEpisodesCallback,
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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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