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: Similar to position++.
docstr_tok: ["Similar","to","position++","."]
code: def _next_position_then_increment(self):
      """Similar to position++."""
      start = self._capacity - 1 if self._use_prioritized_replay else 0
      position = start + self._position
      self._position = (self._position + 1) % self._capacity
      return position
  
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 6 for calling the ReplayMemory obj's underlying member method code with example usage: obj._next_position_then_increment() and returns: position