chore: import upstream snapshot with attribution

rllib/examples/algorithms/ppo/pendulum_ppo.py

@@ -0,0 +1,37 @@
+from ray.rllib.algorithms.ppo import PPOConfig
+from ray.rllib.connectors.env_to_module import MeanStdFilter
+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,
+)
+
+parser = add_rllib_example_script_args(default_timesteps=400000, default_reward=-300)
+# 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()
+
+config = (
+    PPOConfig()
+    .environment("Pendulum-v1")
+    .env_runners(
+        num_env_runners=2,
+        num_envs_per_env_runner=20,
+        env_to_module_connector=lambda env, spaces, device: MeanStdFilter(),
+    )
+    .training(
+        train_batch_size_per_learner=1024,
+        minibatch_size=128,
+        lr=0.0002 * (args.num_learners or 1) ** 0.5,
+        gamma=0.95,
+        lambda_=0.5,
+        # num_epochs=8,
+    )
+    .rl_module(
+        model_config=DefaultModelConfig(fcnet_activation="relu"),
+    )
+)
+
+
+if __name__ == "__main__":
+    run_rllib_example_script_experiment(config, args)