wehub-resource-sync
104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
import warnings
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from pathlib import Path
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from ray.rllib.algorithms.cql.cql import CQLConfig
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from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
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from ray.rllib.examples.utils import (
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add_rllib_example_script_args,
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run_rllib_example_script_experiment,
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)
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from ray.rllib.utils.metrics import (
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ENV_RUNNER_RESULTS,
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EPISODE_RETURN_MEAN,
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EVALUATION_RESULTS,
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NUM_ENV_STEPS_SAMPLED_LIFETIME,
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)
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parser = add_rllib_example_script_args()
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# Use `parser` to add your own custom command line options to this script
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# and (if needed) use their values to set up `config` below.
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args = parser.parse_args()
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assert (
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args.env == "Pendulum-v1" or args.env is None
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), "This tuned example works only with `Pendulum-v1`."
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# Define the base path relative to this file.
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base_path = Path(__file__).parents[3]
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# Use the larger data set of Pendulum we have. Note, these are
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# parquet data, the default in `AlgorithmConfig.offline_data`.
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data_path = base_path / "offline/tests/data/pendulum/pendulum-v1_enormous"
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data_path_uri = f"local://{data_path.as_posix()}"
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print(f"data_path_uri={data_path_uri}")
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# Define the configuration.
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config = (
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CQLConfig()
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.environment("Pendulum-v1")
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.offline_data(
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input_=[data_path_uri],
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# The `kwargs` for the `map_batches` method in which our
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# `OfflinePreLearner` is run. 2 data workers should be run
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# concurrently.
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map_batches_kwargs={"concurrency": 2, "num_cpus": 1},
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# The `kwargs` for the `iter_batches` method. Due to the small
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# dataset we choose only a single batch to prefetch.
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iter_batches_kwargs={"prefetch_batches": 1},
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# The number of iterations to be run per learner when in multi-learner
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# mode in a single RLlib training iteration. Leave this to `None` to
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# run an entire epoch on the dataset during a single RLlib training
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# iteration.
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dataset_num_iters_per_learner=5,
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# TODO (sven): Has this any influence in the connectors?
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actions_in_input_normalized=True,
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)
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.training(
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bc_iters=200,
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tau=9.5e-3,
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min_q_weight=5.0,
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train_batch_size_per_learner=1024,
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twin_q=True,
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actor_lr=1.7e-3 * (args.num_learners or 1) ** 0.5,
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critic_lr=2.5e-3 * (args.num_learners or 1) ** 0.5,
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alpha_lr=1e-3 * (args.num_learners or 1) ** 0.5,
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# Set this to `None` for all `SAC`-like algorithms. These
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# algorithms use learning rates for each optimizer.
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lr=None,
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)
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.reporting(
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min_time_s_per_iteration=10,
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metrics_num_episodes_for_smoothing=5,
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)
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.rl_module(
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model_config=DefaultModelConfig(
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fcnet_hiddens=[256, 256],
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fcnet_activation="relu",
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fusionnet_hiddens=[256, 256, 256],
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fusionnet_activation="relu",
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)
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)
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.evaluation(
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evaluation_interval=3,
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evaluation_num_env_runners=1,
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evaluation_duration=5,
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evaluation_config={
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"explore": False,
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},
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)
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)
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if not args.no_tune:
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warnings.warn(
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"You are running the example with Ray Tune. Offline RL uses "
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"Ray Data, which doesn't does not interact seamlessly with Ray Tune. "
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"If you encounter difficulties try to run the example without "
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"Ray Tune using `--no-tune`."
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)
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stop = {
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f"{EVALUATION_RESULTS}/{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": -700.0,
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NUM_ENV_STEPS_SAMPLED_LIFETIME: 800000,
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}
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if __name__ == "__main__":
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run_rllib_example_script_experiment(config, args, stop=stop)
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