hunk.code_window:             loss_inputs=self.loss_inputs,
               update_ops=q_batchnorm_update_ops)
           self.sess.run(tf.global_variables_initializer())
       @override(TFPolicyGraph)
       def optimizer(self):
           return tf.train.AdamOptimizer(
  
hunk.labels: keep keep keep add keep keep keep
hunk.after_edit:         self.stats_fetches = dict({
               "cur_lr": tf.cast(self.cur_lr, tf.float64),
           }, **self.loss.stats)
  
hunk.file_path: python/ray/rllib/agents/dqn/dqn_policy_graph.py
hunk.type: add
file: humanoid-ppo-gae:
      env: Humanoid-v1
      run: PPO
      stop:
          episode_reward_mean: 6000
      config:
          gamma: 0.995
          lambda: 0.95
          clip_param: 0.2
          kl_coeff: 1.0
          num_sgd_iter: 20
          lr: .0001
          sgd_minibatch_size: 32768
          horizon: 5000
          train_batch_size: 320000
          model:
              free_log_std: true
          num_workers: 64
          num_gpus: 4
          batch_mode: complete_episodes
          observation_filter: MeanStdFilter
  