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https://github.com/vale981/ray
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fcf897ee72
update rllib example to use Tuner API. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
"""Example of handling variable length and/or parametric action spaces.
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This is a toy example of the action-embedding based approach for handling large
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discrete action spaces (potentially infinite in size), similar to this:
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https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five/
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This currently works with RLlib's policy gradient style algorithms
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(e.g., PG, PPO, IMPALA, A2C) and also DQN.
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Note that since the model outputs now include "-inf" tf.float32.min
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values, not all algorithm options are supported at the moment. For example,
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algorithms might crash if they don't properly ignore the -inf action scores.
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Working configurations are given below.
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"""
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import argparse
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import os
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import ray
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from ray import air, tune
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from ray.rllib.examples.env.parametric_actions_cartpole import ParametricActionsCartPole
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from ray.rllib.examples.models.parametric_actions_model import (
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ParametricActionsModel,
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TorchParametricActionsModel,
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)
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from ray.rllib.models import ModelCatalog
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from ray.rllib.utils.test_utils import check_learning_achieved
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from ray.tune.registry import register_env
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--run", type=str, default="PPO", help="The RLlib-registered algorithm to use."
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)
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parser.add_argument(
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"--framework",
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choices=["tf", "tf2", "tfe", "torch"],
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default="tf",
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help="The DL framework specifier.",
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)
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parser.add_argument(
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"--as-test",
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action="store_true",
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help="Whether this script should be run as a test: --stop-reward must "
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"be achieved within --stop-timesteps AND --stop-iters.",
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)
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parser.add_argument(
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"--stop-iters", type=int, default=200, help="Number of iterations to train."
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)
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parser.add_argument(
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"--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
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)
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parser.add_argument(
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"--stop-reward", type=float, default=150.0, help="Reward at which we stop training."
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)
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init()
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register_env("pa_cartpole", lambda _: ParametricActionsCartPole(10))
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ModelCatalog.register_custom_model(
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"pa_model",
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TorchParametricActionsModel
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if args.framework == "torch"
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else ParametricActionsModel,
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)
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if args.run == "DQN":
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cfg = {
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# TODO(ekl) we need to set these to prevent the masked values
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# from being further processed in DistributionalQModel, which
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# would mess up the masking. It is possible to support these if we
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# defined a custom DistributionalQModel that is aware of masking.
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"hiddens": [],
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"dueling": False,
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}
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else:
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cfg = {}
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config = dict(
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{
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"env": "pa_cartpole",
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"model": {
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"custom_model": "pa_model",
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},
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# Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"num_workers": 0,
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"framework": args.framework,
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},
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**cfg
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)
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stop = {
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"training_iteration": args.stop_iters,
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"timesteps_total": args.stop_timesteps,
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"episode_reward_mean": args.stop_reward,
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}
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results = tune.Tuner(
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args.run,
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run_config=air.RunConfig(stop=stop, verbose=1),
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param_space=config,
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).fit()
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if args.as_test:
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check_learning_achieved(results, args.stop_reward)
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ray.shutdown()
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