ray/rllib/examples/batch_norm_model.py

"""Example of using a custom model with batch norm."""

import argparse
import os

import ray
from ray import tune
from ray.rllib.examples.models.batch_norm_model import BatchNormModel, \
    KerasBatchNormModel, TorchBatchNormModel
from ray.rllib.models import ModelCatalog
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.test_utils import check_learning_achieved

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=200,
    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=150.0,
    help="Reward at which we stop training.")

if __name__ == "__main__":
    args = parser.parse_args()
    ray.init()

    ModelCatalog.register_custom_model(
        "bn_model", TorchBatchNormModel
        if args.framework == "torch" else KerasBatchNormModel
        if args.run != "PPO" else BatchNormModel)

    config = {
        "env": "Pendulum-v0" if args.run in ["DDPG", "SAC"] else "CartPole-v0",
        "model": {
            "custom_model": "bn_model",
        },
        "lr": 0.0003,
        # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
        "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
        "num_workers": 0,
        "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, stop=stop, config=config, verbose=2)

    if args.as_test:
        check_learning_achieved(results, args.stop_reward)

    ray.shutdown()