hunk.code_window:     return experiment_state
   def list_trials(experiment_path,
                   sort=None,
                   info_keys=DEFAULT_EXPERIMENT_INFO_KEYS,
                   result_keys=DEFAULT_RESULT_KEYS):
       """Lists trials in the directory subtree starting at the given path.
       Args:
  
hunk.labels: keep keep keep keep add keep keep keep keep keep
hunk.after_edit:                 output=None,
  
hunk.file_path: python/ray/tune/commands.py
hunk.type: add
file: """Integration test: (1) pendulum works, (2) single-agent multi-agent works."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import ray
  from ray.rllib.tests.test_multi_agent_env import make_multiagent
  from ray.tune import run_experiments
  from ray.tune.registry import register_env
  
  if __name__ == "__main__":
      ray.init()
      MultiPendulum = make_multiagent("Pendulum-v0")
      register_env("multi_pend", lambda _: MultiPendulum(1))
      trials = run_experiments({
          "test": {
              "run": "PPO",
              "env": "multi_pend",
              "stop": {
                  "timesteps_total": 500000,
                  "episode_reward_mean": -200,
              },
              "config": {
                  "train_batch_size": 2048,
                  "vf_clip_param": 10.0,
                  "num_workers": 0,
                  "num_envs_per_worker": 10,
                  "lambda": 0.1,
                  "gamma": 0.95,
                  "lr": 0.0003,
                  "sgd_minibatch_size": 64,
                  "num_sgd_iter": 10,
                  "model": {
                      "fcnet_hiddens": [64, 64],
                  },
                  "batch_mode": "complete_episodes",
              },
          }
      })
      if trials[0].last_result["episode_reward_mean"] < -200:
          raise ValueError("Did not get to -200 reward", trials[0].last_result)
  