ray/rllib/examples/parametric_actions_cartpole.py

"""Example of handling variable length and/or parametric action spaces.

This is a toy example of the action-embedding based approach for handling large
discrete action spaces (potentially infinite in size), similar to this:

    https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five/

This currently works with RLlib's policy gradient style algorithms
(e.g., PG, PPO, IMPALA, A2C) and also DQN.

Note that since the model outputs now include "-inf" tf.float32.min
values, not all algorithm options are supported at the moment. For example,
algorithms might crash if they don't properly ignore the -inf action scores.
Working configurations are given below.
"""

import argparse

import ray
from ray import tune
from ray.rllib.examples.env.parametric_actions_cartpole import \
    ParametricActionsCartPole
from ray.rllib.examples.models.parametric_actions_model import \
    ParametricActionsModel, TorchParametricActionsModel
from ray.rllib.models import ModelCatalog
from ray.rllib.utils.test_utils import check_learning_achieved
from ray.tune.registry import register_env

parser = argparse.ArgumentParser()
parser.add_argument("--run", type=str, default="PPO")
parser.add_argument("--torch", action="store_true")
parser.add_argument("--as-test", action="store_true")
parser.add_argument("--stop-iters", type=int, default=200)
parser.add_argument("--stop-reward", type=float, default=150.0)
parser.add_argument("--stop-timesteps", type=int, default=100000)

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

    register_env("pa_cartpole", lambda _: ParametricActionsCartPole(10))
    ModelCatalog.register_custom_model(
        "pa_model", TorchParametricActionsModel
        if args.torch else ParametricActionsModel)

    if args.run == "DQN":
        cfg = {
            # TODO(ekl) we need to set these to prevent the masked values
            # from being further processed in DistributionalQModel, which
            # would mess up the masking. It is possible to support these if we
            # defined a a custom DistributionalQModel that is aware of masking.
            "hiddens": [],
            "dueling": False,
        }
    else:
        cfg = {}

    config = dict({
        "env": "pa_cartpole",
        "model": {
            "custom_model": "pa_model",
        },
        "num_workers": 0,
        "framework": "torch" if args.torch else "tf",
    }, **cfg)

    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)

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

    ray.shutdown()