chore: import upstream snapshot with attribution

rllib/examples/algorithms/appo/pendulum_appo.py

@@ -0,0 +1,48 @@
+from ray.rllib.algorithms.appo import APPOConfig
+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_reward=-300.0,
+    default_timesteps=100000000,
+)
+parser.set_defaults(
+    num_env_runners=4,
+)
+# 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 = (
+    APPOConfig()
+    .environment("Pendulum-v1")
+    .env_runners(
+        num_envs_per_env_runner=20,
+    )
+    .learners(num_learners=1)
+    .training(
+        train_batch_size_per_learner=500,
+        circular_buffer_num_batches=16,
+        circular_buffer_iterations_per_batch=10,
+        target_network_update_freq=2,
+        clip_param=0.4,
+        lr=0.0003,
+        gamma=0.95,
+        lambda_=0.5,
+        entropy_coeff=0.0,
+        use_kl_loss=True,
+        kl_coeff=1.0,
+        kl_target=0.04,
+    )
+    .rl_module(
+        model_config=DefaultModelConfig(fcnet_activation="relu"),
+    )
+)
+
+
+if __name__ == "__main__":
+    run_rllib_example_script_experiment(config, args)