class_docstr: Replay memory to store samples of experience.
  
  Each transition is represented as (state, action, reward, next_state,
  priority) tuple. 'priority' is ignored for non-prioritized experience
  replay.
class_docstr_tok: ["Replay","memory","to","store","samples","of","experience",".","Each","transition","is","represented","as","(","state",",","action",",","reward",",","next_state",",","priority",")","tuple",".","'priority","'","is","ignored","for","non-prioritized","experience","replay","."]
docstr: Update internal priority sums when leaf priority has been changed.
  
  Args:
      index: leaf node index
      delta: change in priority
docstr_tok: ["Update","internal","priority","sums","when","leaf","priority","has","been","changed",".","Args",":","index",":","leaf","node","index","delta",":","change","in","priority"]
code: def _update_internal_nodes(self, index, delta):
      """Update internal priority sums when leaf priority has been changed.
  
      Args:
          index: leaf node index
          delta: change in priority
      """
      while index > 0:
          index = (index - 1) // 2
          self._memory[index] += delta
  
file_path: CNTK/bindings/python/cntk/contrib/deeprl/agent/shared/replay_memory.py
imports: from __future__ import division
  import math
  import random
  from collections import namedtuple
label_desc: Use image node_id 7 for calling the ReplayMemory obj's underlying member method code with example usage: obj._update_internal_nodes(index, delta) without return types