mirror of
https://github.com/vale981/ray
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86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
import argparse
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from gym.spaces import Dict, Tuple, Box, Discrete
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import os
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import ray
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import ray.tune as tune
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from ray.tune.registry import register_env
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from ray.rllib.examples.env.nested_space_repeat_after_me_env import (
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NestedSpaceRepeatAfterMeEnv,
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)
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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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"--run", type=str, default="PPO", help="The RLlib-registered algorithm to use."
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)
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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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)
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parser.add_argument("--num-cpus", type=int, default=0)
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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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)
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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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)
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parser.add_argument(
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"--stop-iters", type=int, default=100, help="Number of iterations to train."
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)
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parser.add_argument(
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"--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
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)
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parser.add_argument(
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"--stop-reward", type=float, default=0.0, help="Reward at which we stop training."
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)
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if __name__ == "__main__":
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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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register_env(
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"NestedSpaceRepeatAfterMeEnv", lambda c: NestedSpaceRepeatAfterMeEnv(c)
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)
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config = {
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"env": "NestedSpaceRepeatAfterMeEnv",
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"env_config": {
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"space": Dict(
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{
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"a": Tuple([Dict({"d": Box(-10.0, 10.0, ()), "e": Discrete(2)})]),
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"b": Box(-10.0, 10.0, (2,)),
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"c": Discrete(4),
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}
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),
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},
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"entropy_coeff": 0.00005, # We don't want high entropy in this Env.
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"gamma": 0.0, # No history in Env (bandit problem).
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"lr": 0.0005,
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"num_envs_per_worker": 20,
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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_sgd_iter": 4,
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"num_workers": 0,
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"vf_loss_coeff": 0.01,
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"framework": args.framework,
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}
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stop = {
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"training_iteration": args.stop_iters,
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"episode_reward_mean": args.stop_reward,
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"timesteps_total": args.stop_timesteps,
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}
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results = tune.run(args.run, config=config, stop=stop, verbose=1)
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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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