mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
import argparse
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import ray
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from ray import tune
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from ray.rllib.utils import try_import_tf
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from ray.rllib.models.tf.attention_net import GTrXLNet
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from ray.rllib.examples.env.look_and_push import LookAndPush, OneHot
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from ray.rllib.examples.env.repeat_after_me_env import RepeatAfterMeEnv
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from ray.rllib.examples.env.repeat_initial_obs_env import RepeatInitialObsEnv
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from ray.rllib.examples.env.stateless_cartpole import StatelessCartPole
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from ray.rllib.utils.test_utils import check_learning_achieved
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from ray.tune import registry
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tf = try_import_tf()
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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("--env", type=str, default="RepeatAfterMeEnv")
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parser.add_argument("--num-cpus", type=int, default=0)
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parser.add_argument("--torch", action="store_true")
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parser.add_argument("--as-test", action="store_true")
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parser.add_argument("--stop-iters", type=int, default=200)
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parser.add_argument("--stop-timesteps", type=int, default=500000)
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parser.add_argument("--stop-reward", type=float, default=80)
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if __name__ == "__main__":
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args = parser.parse_args()
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assert not args.torch, "PyTorch not supported for AttentionNets yet!"
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ray.init(num_cpus=args.num_cpus or None, local_mode=True)
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registry.register_env("RepeatAfterMeEnv", lambda c: RepeatAfterMeEnv(c))
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registry.register_env("RepeatInitialObsEnv",
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lambda _: RepeatInitialObsEnv())
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registry.register_env("LookAndPush", lambda _: OneHot(LookAndPush()))
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registry.register_env("StatelessCartPole", lambda _: StatelessCartPole())
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config = {
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"env": args.env,
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"env_config": {
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"repeat_delay": 2,
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},
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"gamma": 0.99,
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"num_workers": 0,
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"num_envs_per_worker": 20,
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"entropy_coeff": 0.001,
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"num_sgd_iter": 5,
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"vf_loss_coeff": 1e-5,
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"model": {
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"custom_model": GTrXLNet,
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"max_seq_len": 50,
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"custom_model_config": {
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"num_transformer_units": 1,
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"attn_dim": 64,
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"num_heads": 2,
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"memory_tau": 50,
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"head_dim": 32,
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"ff_hidden_dim": 32,
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},
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},
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"framework": "torch" if args.torch else "tf",
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}
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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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"episode_reward_mean": args.stop_reward,
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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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