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- """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 os
- import ray
- from ray import tune
- from ray.rllib.examples.env.parametric_actions_cartpole import \
- ParametricActionsCartPoleNoEmbeddings
- from ray.rllib.examples.models.parametric_actions_model import \
- ParametricActionsModelThatLearnsEmbeddings
- 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(
- "--framework",
- choices=["tf", "tf2", "tfe"],
- default="tf",
- help="The DL framework specifier (torch not supported yet "
- "due to lack of model).")
- 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 _: ParametricActionsCartPoleNoEmbeddings(10))
- ModelCatalog.register_custom_model(
- "pa_model", ParametricActionsModelThatLearnsEmbeddings)
- 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 custom DistributionalQModel that is aware of masking.
- "hiddens": [],
- "dueling": False,
- }
- else:
- cfg = {}
- config = dict(
- {
- "env": "pa_cartpole",
- "model": {
- "custom_model": "pa_model",
- },
- # 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,
- },
- **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, verbose=2)
- if args.as_test:
- check_learning_achieved(results, args.stop_reward)
- ray.shutdown()
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