ray/rllib/examples/nested_action_spaces.py

import argparse
from gym.spaces import Dict, Tuple, Box, Discrete
import os

import ray
import ray.tune as tune
from ray.tune.registry import register_env
from ray.rllib.examples.env.nested_space_repeat_after_me_env import (
    NestedSpaceRepeatAfterMeEnv,
)
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(
    "--framework",
    choices=["tf", "tf2", "tfe", "torch"],
    default="tf",
    help="The DL framework specifier.",
)
parser.add_argument("--num-cpus", type=int, default=0)
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(
    "--local-mode",
    action="store_true",
    help="Init Ray in local mode for easier debugging.",
)
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=0.0, help="Reward at which we stop training."
)

if __name__ == "__main__":
    args = parser.parse_args()
    ray.init(num_cpus=args.num_cpus or None, local_mode=args.local_mode)
    register_env(
        "NestedSpaceRepeatAfterMeEnv", lambda c: NestedSpaceRepeatAfterMeEnv(c)
    )

    config = {
        "env": "NestedSpaceRepeatAfterMeEnv",
        "env_config": {
            "space": Dict(
                {
                    "a": Tuple([Dict({"d": Box(-10.0, 10.0, ()), "e": Discrete(2)})]),
                    "b": Box(-10.0, 10.0, (2,)),
                    "c": Discrete(4),
                }
            ),
        },
        "entropy_coeff": 0.00005,  # We don't want high entropy in this Env.
        "gamma": 0.0,  # No history in Env (bandit problem).
        "lr": 0.0005,
        "num_envs_per_worker": 20,
        # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
        "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
        "num_sgd_iter": 4,
        "num_workers": 0,
        "vf_loss_coeff": 0.01,
        "framework": args.framework,
    }

    stop = {
        "training_iteration": args.stop_iters,
        "episode_reward_mean": args.stop_reward,
        "timesteps_total": args.stop_timesteps,
    }

    results = tune.run(args.run, config=config, stop=stop, verbose=1)

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

    ray.shutdown()