chore: import upstream snapshot with attribution

rllib/examples/_old_api_stack/parametric_actions_cartpole.py

@@ -0,0 +1,121 @@
+# @OldAPIStack
+"""Example of handling variable length or parametric action spaces.
+
+This toy example demonstrates the action-embedding based approach for handling large
+discrete action spaces (potentially infinite in size), similar to this example:
+
+    https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five/
+
+This example works with RLlib's policy gradient style algorithms
+(e.g., PG, PPO, IMPALA, A2C) and DQN.
+
+Note that since the model outputs now include "-inf" tf.float32.min
+values, not all algorithm options are supported. 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._old_api_stack.models.parametric_actions_model import (
+    ParametricActionsModel,
+    TorchParametricActionsModel,
+)
+from ray.rllib.examples.envs.classes.parametric_actions_cartpole import (
+    ParametricActionsCartPole,
+)
+from ray.rllib.models import ModelCatalog
+from ray.rllib.utils.metrics import (
+    ENV_RUNNER_RESULTS,
+    EPISODE_RETURN_MEAN,
+    NUM_ENV_STEPS_SAMPLED_LIFETIME,
+)
+from ray.rllib.utils.test_utils import check_learning_achieved
+from ray.tune.registry import register_env
+from ray.tune.result import TRAINING_ITERATION
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--run", type=str, default="PPO", help="The RLlib-registered algorithm to use."
+)
+parser.add_argument(
+    "--framework",
+    choices=["tf", "tf2", "torch"],
+    default="torch",
+    help="The DL framework specifier.",
+)
+parser.add_argument(
+    "--as-test",
+    action="store_true",
+    help="Whether this script should be run as a test: --stop-reward must "
+    "be achieved within --stop-timesteps AND --stop-iters.",
+)
+parser.add_argument(
+    "--stop-iters", type=int, default=200, help="Number of iterations to train."
+)
+parser.add_argument(
+    "--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
+)
+parser.add_argument(
+    "--stop-reward", type=float, default=150.0, help="Reward at which we stop training."
+)
+
+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.framework == "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 custom DistributionalQModel that is aware of masking.
+            "hiddens": [],
+            "dueling": False,
+            "enable_rl_module_and_learner": False,
+            "enable_env_runner_and_connector_v2": 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_env_runners": 0,
+            "framework": args.framework,
+        },
+        **cfg,
+    )
+
+    stop = {
+        TRAINING_ITERATION: args.stop_iters,
+        f"{NUM_ENV_STEPS_SAMPLED_LIFETIME}": args.stop_timesteps,
+        f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": args.stop_reward,
+    }
+
+    results = tune.Tuner(
+        args.run,
+        run_config=tune.RunConfig(stop=stop, verbose=1),
+        param_space=config,
+    ).fit()
+
+    if args.as_test:
+        check_learning_achieved(results, args.stop_reward)
+
+    ray.shutdown()