hunk.code_window:   TASK_PUBSUB = 2;
     RAYLET_TASK_PUBSUB = 3;
     NODE_PUBSUB = 4;
     OBJECT_PUBSUB = 5;
     ACTOR_PUBSUB = 6;
     HEARTBEAT_PUBSUB = 7;
     RESOURCE_USAGE_BATCH_PUBSUB = 8;
     TASK_LEASE_PUBSUB = 9;
     JOB_PUBSUB = 10;
     NODE_RESOURCE_PUBSUB = 11;
     DIRECT_ACTOR_PUBSUB = 12;
     WORKER_FAILURE_PUBSUB = 13;
     TABLE_PUBSUB_MAX = 14;
   }
   enum GcsChangeMode {
  
hunk.labels: keep keep keep keep keep replace replace replace replace replace replace replace replace keep keep keep
hunk.after_edit:   RESOURCE_USAGE_BATCH_PUBSUB = 7;
     TASK_LEASE_PUBSUB = 8;
     JOB_PUBSUB = 9;
     NODE_RESOURCE_PUBSUB = 10;
     DIRECT_ACTOR_PUBSUB = 11;
     WORKER_FAILURE_PUBSUB = 12;
     TABLE_PUBSUB_MAX = 13;
  
hunk.file_path: src/ray/protobuf/gcs.proto
hunk.type: replace
file: # Can expect improvement to -140 reward in ~300-500k timesteps.
  pendulum-ppo:
      env: Pendulum-v0
      run: PPO
      stop:
          episode_reward_mean: -500
          timesteps_total: 400000
      config:
          # Works for both torch and tf.
          framework: tf
          train_batch_size: 512
          vf_clip_param: 10.0
          num_workers: 0
          num_envs_per_worker: 20
          lambda: 0.1
          gamma: 0.95
          lr: 0.0003
          sgd_minibatch_size: 64
          num_sgd_iter: 6
          observation_filter: MeanStdFilter
  