chore: import upstream snapshot with attribution

rllib/examples/algorithms/ppo/multi_agent_cartpole_ppo.py

@@ -0,0 +1,57 @@
+from ray.rllib.algorithms.ppo import PPOConfig
+from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
+from ray.rllib.examples.envs.classes.multi_agent import MultiAgentCartPole
+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,
+    NUM_ENV_STEPS_SAMPLED_LIFETIME,
+)
+from ray.tune.registry import register_env
+
+parser = add_rllib_example_script_args()
+parser.set_defaults(
+    num_agents=2,
+)
+# 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()
+
+register_env("multi_agent_cartpole", lambda cfg: MultiAgentCartPole(config=cfg))
+
+config = (
+    PPOConfig()
+    .environment("multi_agent_cartpole", env_config={"num_agents": args.num_agents})
+    .rl_module(
+        model_config=DefaultModelConfig(
+            fcnet_hiddens=[32],
+            fcnet_activation="linear",
+            vf_share_layers=True,
+        ),
+    )
+    .env_runners(
+        num_envs_per_env_runner=2,
+    )
+    .training(
+        lr=0.0003,
+        num_epochs=6,
+        vf_loss_coeff=0.01,
+    )
+    .multi_agent(
+        policy_mapping_fn=lambda aid, *arg, **kw: f"p{aid}",
+        policies={f"p{i}" for i in range(args.num_agents)},
+    )
+)
+
+stop = {
+    NUM_ENV_STEPS_SAMPLED_LIFETIME: 400000,
+    # Divide by num_agents to get actual return per agent.
+    f"{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": 450.0 * (args.num_agents or 1),
+}
+
+
+if __name__ == "__main__":
+    run_rllib_example_script_experiment(config, args, stop=stop)