mirror of
https://github.com/vale981/ray
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62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
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"""
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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 os
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import ray
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from ray import tune
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from ray.rllib.examples.env.env_using_remote_actor import \
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CartPoleWithRemoteParamServer, ParameterStorage
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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from ray.rllib.utils.test_utils import check
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tf1, tf, tfv = try_import_tf()
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torch, nn = try_import_torch()
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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(
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"--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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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init()
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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": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"num_workers": 2, # parallelism
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"framework": args.framework,
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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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results = tune.run(args.run, config=config, stop=stop, verbose=1)
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results2 = tune.run(args.run, config=config, stop=stop, verbose=1)
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if args.as_test:
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check(
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list(results.results.values())[0]["hist_stats"],
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list(results2.results.values())[0]["hist_stats"])
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ray.shutdown()
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