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- """Example of using a custom RNN keras model."""
- import argparse
- import os
- import ray
- from ray import air, tune
- from ray.tune.registry import register_env
- from ray.rllib.examples.env.repeat_after_me_env import RepeatAfterMeEnv
- from ray.rllib.examples.env.repeat_initial_obs_env import RepeatInitialObsEnv
- from ray.rllib.examples.models.rnn_model import RNNModel, TorchRNNModel
- from ray.rllib.models import ModelCatalog
- from ray.rllib.utils.test_utils import check_learning_achieved
- from ray.tune.registry import get_trainable_cls
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--run", type=str, default="PPO", help="The RLlib-registered algorithm to use."
- )
- parser.add_argument("--env", type=str, default="RepeatAfterMeEnv")
- parser.add_argument("--num-cpus", type=int, default=0)
- parser.add_argument(
- "--framework",
- choices=["tf", "tf2", "torch"],
- default="torch",
- help="The DL framework specifier.",
- )
- parser.add_argument(
- "--as-test",
- action="store_true",
- help="Whether this script should be run as a test: --stop-reward must "
- "be achieved within --stop-timesteps AND --stop-iters.",
- )
- parser.add_argument(
- "--stop-iters", type=int, default=100, help="Number of iterations to train."
- )
- parser.add_argument(
- "--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
- )
- parser.add_argument(
- "--stop-reward", type=float, default=90.0, help="Reward at which we stop training."
- )
- parser.add_argument(
- "--local-mode",
- action="store_true",
- help="Init Ray in local mode for easier debugging.",
- )
- if __name__ == "__main__":
- args = parser.parse_args()
- ray.init(num_cpus=args.num_cpus or None, local_mode=args.local_mode)
- ModelCatalog.register_custom_model(
- "rnn", TorchRNNModel if args.framework == "torch" else RNNModel
- )
- register_env("RepeatAfterMeEnv", lambda c: RepeatAfterMeEnv(c))
- register_env("RepeatInitialObsEnv", lambda _: RepeatInitialObsEnv())
- config = (
- get_trainable_cls(args.run)
- .get_default_config()
- .environment(args.env, env_config={"repeat_delay": 2})
- .framework(args.framework)
- .rollouts(num_rollout_workers=0, num_envs_per_worker=20)
- .training(
- model={
- "custom_model": "rnn",
- "max_seq_len": 20,
- "custom_model_config": {
- "cell_size": 32,
- },
- },
- gamma=0.9,
- # TODO (Kourosh): Enable when LSTMs are supported.
- _enable_learner_api=False,
- )
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- .resources(num_gpus=int(os.environ.get("RLLIB_NUM_GPUS", "0")))
- .rl_module(_enable_rl_module_api=False)
- )
- if args.run == "PPO":
- config.training(entropy_coeff=0.001, num_sgd_iter=5, vf_loss_coeff=1e-5)
- stop = {
- "training_iteration": args.stop_iters,
- "timesteps_total": args.stop_timesteps,
- "episode_reward_mean": args.stop_reward,
- }
- # To run the Algorithm without ``Tuner.fit()``, using our RNN model and
- # manual state-in handling, do the following:
- # Example (use `config` from the above code):
- # >> import numpy as np
- # >> from ray.rllib.algorithms.ppo import PPO
- # >>
- # >> algo = config.build()
- # >> lstm_cell_size = config.model["custom_model_config"]["cell_size"]
- # >> env = RepeatAfterMeEnv({})
- # >> obs, info = env.reset()
- # >>
- # >> # range(2) b/c h- and c-states of the LSTM.
- # >> init_state = state = [
- # .. np.zeros([lstm_cell_size], np.float32) for _ in range(2)
- # .. ]
- # >>
- # >> while True:
- # >> a, state_out, _ = algo.compute_single_action(obs, state)
- # >> obs, reward, done, _, _ = env.step(a)
- # >> if done:
- # >> obs, info = env.reset()
- # >> state = init_state
- # >> else:
- # >> state = state_out
- tuner = tune.Tuner(
- args.run,
- param_space=config.to_dict(),
- run_config=air.RunConfig(stop=stop, verbose=1),
- )
- results = tuner.fit()
- if args.as_test:
- check_learning_achieved(results, args.stop_reward)
- ray.shutdown()
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