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- import argparse
- import numpy as np
- import ray
- from ray.rllib.agents.ppo import PPOTrainer
- from ray.rllib.examples.env.stateless_cartpole import StatelessCartPole
- from ray.rllib.examples.models.trajectory_view_utilizing_models import \
- FrameStackingCartPoleModel, TorchFrameStackingCartPoleModel
- from ray.rllib.models.catalog import ModelCatalog
- from ray.rllib.utils.framework import try_import_tf
- from ray.rllib.utils.test_utils import check_learning_achieved
- from ray import tune
- tf1, tf, tfv = try_import_tf()
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--run",
- type=str,
- default="PPO",
- help="The RLlib-registered algorithm to use.")
- 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=50,
- help="Number of iterations to train.")
- parser.add_argument(
- "--stop-timesteps",
- type=int,
- default=200000,
- help="Number of timesteps to train.")
- parser.add_argument(
- "--stop-reward",
- type=float,
- default=150.0,
- help="Reward at which we stop training.")
- if __name__ == "__main__":
- args = parser.parse_args()
- ray.init(num_cpus=3)
- num_frames = 16
- ModelCatalog.register_custom_model(
- "frame_stack_model", FrameStackingCartPoleModel
- if args.framework != "torch" else TorchFrameStackingCartPoleModel)
- config = {
- "env": StatelessCartPole,
- "model": {
- "vf_share_layers": True,
- "custom_model": "frame_stack_model",
- "custom_model_config": {
- "num_frames": num_frames,
- },
- # To compare against a simple LSTM:
- # "use_lstm": True,
- # "lstm_use_prev_action": True,
- # "lstm_use_prev_reward": True,
- # To compare against a simple attention net:
- # "use_attention": True,
- # "attention_use_n_prev_actions": 1,
- # "attention_use_n_prev_rewards": 1,
- },
- "num_sgd_iter": 5,
- "vf_loss_coeff": 0.0001,
- "framework": args.framework,
- }
- stop = {
- "training_iteration": args.stop_iters,
- "timesteps_total": args.stop_timesteps,
- "episode_reward_mean": args.stop_reward,
- }
- results = tune.run(
- args.run, config=config, stop=stop, verbose=2, checkpoint_at_end=True)
- if args.as_test:
- check_learning_achieved(results, args.stop_reward)
- checkpoints = results.get_trial_checkpoints_paths(
- trial=results.get_best_trial("episode_reward_mean", mode="max"),
- metric="episode_reward_mean")
- checkpoint_path = checkpoints[0][0]
- trainer = PPOTrainer(config)
- trainer.restore(checkpoint_path)
- # Inference loop.
- env = StatelessCartPole()
- # Run manual inference loop for n episodes.
- for _ in range(10):
- episode_reward = 0.0
- reward = 0.0
- action = 0
- done = False
- obs = env.reset()
- while not done:
- # Create a dummy action using the same observation n times,
- # as well as dummy prev-n-actions and prev-n-rewards.
- action, state, logits = trainer.compute_single_action(
- input_dict={
- "obs": obs,
- "prev_n_obs": np.stack([obs for _ in range(num_frames)]),
- "prev_n_actions": np.stack([0 for _ in range(num_frames)]),
- "prev_n_rewards": np.stack(
- [1.0 for _ in range(num_frames)]),
- },
- full_fetch=True)
- obs, reward, done, info = env.step(action)
- episode_reward += reward
- print(f"Episode reward={episode_reward}")
- ray.shutdown()
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