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>
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
"""
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Example of a fully deterministic, repeatable RLlib train run using
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the "seed" config key.
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"""
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import argparse
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import ray
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from ray import air, tune
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from ray.rllib.examples.env.env_using_remote_actor import (
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CartPoleWithRemoteParamServer,
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ParameterStorage,
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)
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from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
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from ray.rllib.utils.metrics.learner_info import LEARNER_INFO
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from ray.rllib.utils.test_utils import check
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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("--framework", choices=["tf2", "tf", "tfe", "torch"], default="tf")
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parser.add_argument("--seed", type=int, default=42)
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parser.add_argument("--as-test", action="store_true")
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parser.add_argument("--stop-iters", type=int, default=2)
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parser.add_argument("--num-gpus", type=float, default=0)
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parser.add_argument("--num-gpus-per-worker", type=float, default=0)
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if __name__ == "__main__":
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args = parser.parse_args()
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param_storage = ParameterStorage.options(name="param-server").remote()
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config = {
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"env": CartPoleWithRemoteParamServer,
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"env_config": {
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"param_server": "param-server",
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},
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": args.num_gpus,
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"num_workers": 1, # parallelism
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"num_gpus_per_worker": args.num_gpus_per_worker,
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"num_envs_per_worker": 2,
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"framework": args.framework,
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# Make sure every environment gets a fixed seed.
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"seed": args.seed,
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# Simplify to run this example script faster.
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"train_batch_size": 100,
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"sgd_minibatch_size": 10,
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"num_sgd_iter": 5,
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"rollout_fragment_length": 50,
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}
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stop = {
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"training_iteration": args.stop_iters,
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}
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results1 = tune.Tuner(
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args.run, param_space=config, run_config=air.RunConfig(stop=stop, verbose=1)
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).fit()
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results2 = tune.Tuner(
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args.run, param_space=config, run_config=air.RunConfig(stop=stop, verbose=1)
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).fit()
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if args.as_test:
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results1 = results1.get_best_result().metrics
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results2 = results2.get_best_result().metrics
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# Test rollout behavior.
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check(results1["hist_stats"], results2["hist_stats"])
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# As well as training behavior (minibatch sequence during SGD
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# iterations).
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check(
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results1["info"][LEARNER_INFO][DEFAULT_POLICY_ID]["learner_stats"],
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results2["info"][LEARNER_INFO][DEFAULT_POLICY_ID]["learner_stats"],
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)
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ray.shutdown()
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