chore: import upstream snapshot with attribution

rllib/examples/algorithms/cql/pendulum_cql.py

@@ -0,0 +1,103 @@
+import warnings
+from pathlib import Path
+
+from ray.rllib.algorithms.cql.cql import CQLConfig
+from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
+from ray.rllib.examples.utils import (
+    add_rllib_example_script_args,
+    run_rllib_example_script_experiment,
+)
+from ray.rllib.utils.metrics import (
+    ENV_RUNNER_RESULTS,
+    EPISODE_RETURN_MEAN,
+    EVALUATION_RESULTS,
+    NUM_ENV_STEPS_SAMPLED_LIFETIME,
+)
+
+parser = add_rllib_example_script_args()
+# Use `parser` to add your own custom command line options to this script
+# and (if needed) use their values to set up `config` below.
+args = parser.parse_args()
+
+assert (
+    args.env == "Pendulum-v1" or args.env is None
+), "This tuned example works only with `Pendulum-v1`."
+
+# Define the base path relative to this file.
+base_path = Path(__file__).parents[3]
+# Use the larger data set of Pendulum we have. Note, these are
+# parquet data, the default in `AlgorithmConfig.offline_data`.
+data_path = base_path / "offline/tests/data/pendulum/pendulum-v1_enormous"
+data_path_uri = f"local://{data_path.as_posix()}"
+print(f"data_path_uri={data_path_uri}")
+# Define the configuration.
+config = (
+    CQLConfig()
+    .environment("Pendulum-v1")
+    .offline_data(
+        input_=[data_path_uri],
+        # The `kwargs` for the `map_batches` method in which our
+        # `OfflinePreLearner` is run. 2 data workers should be run
+        # concurrently.
+        map_batches_kwargs={"concurrency": 2, "num_cpus": 1},
+        # The `kwargs` for the `iter_batches` method. Due to the small
+        # dataset we choose only a single batch to prefetch.
+        iter_batches_kwargs={"prefetch_batches": 1},
+        # The number of iterations to be run per learner when in multi-learner
+        # mode in a single RLlib training iteration. Leave this to `None` to
+        # run an entire epoch on the dataset during a single RLlib training
+        # iteration.
+        dataset_num_iters_per_learner=5,
+        # TODO (sven): Has this any influence in the connectors?
+        actions_in_input_normalized=True,
+    )
+    .training(
+        bc_iters=200,
+        tau=9.5e-3,
+        min_q_weight=5.0,
+        train_batch_size_per_learner=1024,
+        twin_q=True,
+        actor_lr=1.7e-3 * (args.num_learners or 1) ** 0.5,
+        critic_lr=2.5e-3 * (args.num_learners or 1) ** 0.5,
+        alpha_lr=1e-3 * (args.num_learners or 1) ** 0.5,
+        # Set this to `None` for all `SAC`-like algorithms. These
+        # algorithms use learning rates for each optimizer.
+        lr=None,
+    )
+    .reporting(
+        min_time_s_per_iteration=10,
+        metrics_num_episodes_for_smoothing=5,
+    )
+    .rl_module(
+        model_config=DefaultModelConfig(
+            fcnet_hiddens=[256, 256],
+            fcnet_activation="relu",
+            fusionnet_hiddens=[256, 256, 256],
+            fusionnet_activation="relu",
+        )
+    )
+    .evaluation(
+        evaluation_interval=3,
+        evaluation_num_env_runners=1,
+        evaluation_duration=5,
+        evaluation_config={
+            "explore": False,
+        },
+    )
+)
+
+if not args.no_tune:
+    warnings.warn(
+        "You are running the example with Ray Tune. Offline RL uses "
+        "Ray Data, which doesn't does not interact seamlessly with Ray Tune. "
+        "If you encounter difficulties try to run the example without "
+        "Ray Tune using `--no-tune`."
+    )
+
+stop = {
+    f"{EVALUATION_RESULTS}/{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": -700.0,
+    NUM_ENV_STEPS_SAMPLED_LIFETIME: 800000,
+}
+
+if __name__ == "__main__":
+    run_rllib_example_script_experiment(config, args, stop=stop)