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: Actual capacity needed.
  
  For prioritized replay, this includes both leaf nodes containing
  transitions and internal nodes containing priority sum.
docstr_tok: ["Actual","capacity","needed",".","For","prioritized","replay",",","this","includes","both","leaf","nodes","containing","transitions","and","internal","nodes","containing","priority","sum","."]
code: def _actual_capacity(self):
      """Actual capacity needed.
  
      For prioritized replay, this includes both leaf nodes containing
      transitions and internal nodes containing priority sum.
      """
      return (
          2 * self._capacity - 1
          if self._use_prioritized_replay
          else self._capacity
      )
  
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 4 for calling the ReplayMemory obj's underlying member method code with example usage: obj._actual_capacity() and returns: unknown