hunk.code_window:                 shape=[low_action.size], mean=0.0, stddev=1.0)
               exploration_value = tf.assign_add(
                   exploration_sample,
                   theta * (.0 - exploration_sample) + sigma * normal_sample)
               stochastic_actions = deterministic_actions + eps * (
                   high_action - low_action) * exploration_value
           self.actions = tf.cond(stochastic, lambda: stochastic_actions,
                                  lambda: deterministic_actions)
  
hunk.labels: keep keep keep keep replace replace keep keep keep
hunk.after_edit:             stochastic_actions = tf.clip_by_value(
                   deterministic_actions +
                   eps * (high_action - low_action) * exploration_value,
                   low_action, high_action)
  
hunk.file_path: python/ray/rllib/agents/ddpg/ddpg_policy_graph.py
hunk.type: replace
file: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from ray.tune.schedulers.trial_scheduler import TrialScheduler, FIFOScheduler
  from ray.tune.schedulers.hyperband import HyperBandScheduler
  from ray.tune.schedulers.async_hyperband import AsyncHyperBandScheduler
  from ray.tune.schedulers.median_stopping_rule import MedianStoppingRule
  from ray.tune.schedulers.pbt import PopulationBasedTraining
  
  __all__ = [
      "TrialScheduler", "HyperBandScheduler", "AsyncHyperBandScheduler",
      "MedianStoppingRule", "FIFOScheduler", "PopulationBasedTraining"
  ]
  