mirror of
https://github.com/vale981/ray
synced 2025-03-09 12:56:46 -04:00
42991d723f
Cleans up of the rllib/examples folder by moving all example Envs into rllibexamples/env (so they can be used by other scripts and tests as well).
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
import argparse
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from gym.spaces import Dict, Tuple, Box, Discrete
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import sys
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import ray
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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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from ray.rllib.utils import try_import_tree
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from ray.rllib.utils.framework import try_import_tf
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tf = try_import_tf()
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tree = try_import_tree()
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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("--torch", action="store_true")
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parser.add_argument("--stop", type=int, default=90)
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parser.add_argument("--max-trainstop", type=int, default=90)
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parser.add_argument("--num-cpus", type=int, default=0)
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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)
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register_env("NestedSpaceRepeatAfterMeEnv",
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lambda c: NestedSpaceRepeatAfterMeEnv(c))
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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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"a": Tuple(
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[Dict({
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"d": Box(-10.0, 10.0, ()),
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"e": Discrete(2)
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})]),
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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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"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": 0.0003,
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"num_envs_per_worker": 20,
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"num_sgd_iter": 20,
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"num_workers": 0,
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"use_pytorch": args.torch,
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"vf_loss_coeff": 0.01,
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}
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import ray.rllib.agents.ppo as ppo
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trainer = ppo.PPOTrainer(config=config)
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for _ in range(100):
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results = trainer.train()
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print(results)
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if results["episode_reward_mean"] > args.stop:
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sys.exit(0) # Learnt, exit gracefully.
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sys.exit(1) # Done, but did not learn, exit with error.
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