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- """Example of using a custom RNN keras model."""
- import argparse
- import os
- import ray
- from ray import 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
- 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", "tfe", "torch"],
- default="tf",
- 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.")
- if __name__ == "__main__":
- args = parser.parse_args()
- ray.init(num_cpus=args.num_cpus or None)
- 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 = {
- "env": args.env,
- "env_config": {
- "repeat_delay": 2,
- },
- "gamma": 0.9,
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
- "num_workers": 0,
- "num_envs_per_worker": 20,
- "entropy_coeff": 0.001,
- "num_sgd_iter": 5,
- "vf_loss_coeff": 1e-5,
- "model": {
- "custom_model": "rnn",
- "max_seq_len": 20,
- "custom_model_config": {
- "cell_size": 32,
- },
- },
- "framework": args.framework,
- }
- stop = {
- "training_iteration": args.stop_iters,
- "timesteps_total": args.stop_timesteps,
- "episode_reward_mean": args.stop_reward,
- }
- # To run the Trainer without tune.run, 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.agents.ppo import PPOTrainer
- # >>
- # >> trainer = PPOTrainer(config)
- # >> lstm_cell_size = config["model"]["custom_model_config"]["cell_size"]
- # >> env = RepeatAfterMeEnv({})
- # >> obs = 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, _ = trainer.compute_single_action(obs, state)
- # >> obs, reward, done, _ = env.step(a)
- # >> if done:
- # >> obs = env.reset()
- # >> state = init_state
- # >> else:
- # >> state = state_out
- results = tune.run(args.run, config=config, stop=stop, verbose=1)
- if args.as_test:
- check_learning_achieved(results, args.stop_reward)
- ray.shutdown()
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