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https://github.com/wassname/ray.git
synced 2026-07-06 05:16:30 +08:00
Eric Liang
41 lines
1.3 KiB
Python
Executable File
41 lines
1.3 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import ray
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from ray.rllib.evolution_strategies import EvolutionStrategies, DEFAULT_CONFIG
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Train an RL agent on Pong.")
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parser.add_argument("--num-workers", default=10, type=int,
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help=("The number of actors to create in aggregate "
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"across the cluster."))
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parser.add_argument("--env-name", default="Pendulum-v0", type=str,
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help="The name of the gym environment to use.")
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parser.add_argument("--stepsize", default=0.01, type=float,
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help="The stepsize to use.")
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parser.add_argument("--redis-address", default=None, type=str,
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help="The Redis address of the cluster.")
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args = parser.parse_args()
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num_workers = args.num_workers
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env_name = args.env_name
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stepsize = args.stepsize
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ray.init(redis_address=args.redis_address,
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num_workers=(0 if args.redis_address is None else None))
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config = DEFAULT_CONFIG._replace(
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num_workers=num_workers,
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stepsize=stepsize)
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alg = EvolutionStrategies(env_name, config)
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while True:
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result = alg.train()
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print("current status: {}".format(result))
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