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- # Explains/tests Issues:
- # https://github.com/ray-project/ray/issues/6928
- # https://github.com/ray-project/ray/issues/6732
- import argparse
- from gym.spaces import Discrete, Box
- import numpy as np
- import os
- from ray import tune
- from ray.rllib.examples.env.random_env import RandomEnv
- from ray.rllib.examples.models.mobilenet_v2_with_lstm_models import \
- MobileV2PlusRNNModel, TorchMobileV2PlusRNNModel
- from ray.rllib.models import ModelCatalog
- from ray.rllib.utils.framework import try_import_tf
- tf1, tf, tfv = try_import_tf()
- cnn_shape = (4, 4, 3)
- # The torch version of MobileNetV2 does channels first.
- cnn_shape_torch = (3, 224, 224)
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--framework",
- choices=["tf", "tf2", "tfe", "torch"],
- default="tf",
- help="The DL framework specifier.")
- parser.add_argument("--stop-iters", type=int, default=200)
- parser.add_argument("--stop-reward", type=float, default=0.0)
- parser.add_argument("--stop-timesteps", type=int, default=100000)
- if __name__ == "__main__":
- args = parser.parse_args()
- # Register our custom model.
- ModelCatalog.register_custom_model(
- "my_model", TorchMobileV2PlusRNNModel
- if args.framework == "torch" else MobileV2PlusRNNModel)
- stop = {
- "training_iteration": args.stop_iters,
- "timesteps_total": args.stop_timesteps,
- "episode_reward_mean": args.stop_reward,
- }
- # Configure our Trainer.
- config = {
- "env": RandomEnv,
- "framework": args.framework,
- "model": {
- "custom_model": "my_model",
- # Extra config passed to the custom model's c'tor as kwargs.
- "custom_model_config": {
- # By default, torch CNNs use "channels-first",
- # tf "channels-last".
- "cnn_shape": cnn_shape_torch
- if args.framework == "torch" else cnn_shape,
- },
- "max_seq_len": 20,
- "vf_share_layers": True,
- },
- # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
- "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
- "num_workers": 0, # no parallelism
- "env_config": {
- "action_space": Discrete(2),
- # Test a simple Image observation space.
- "observation_space": Box(
- 0.0,
- 1.0,
- shape=cnn_shape_torch
- if args.framework == "torch" else cnn_shape,
- dtype=np.float32)
- },
- }
- tune.run("PPO", config=config, stop=stop, verbose=1)
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