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: Store a transition in replay memory.
  
  If the memory is full, the oldest one gets overwritten.
docstr_tok: ["Store","a","transition","in","replay","memory",".","If","the","memory","is","full",",","the","oldest","one","gets","overwritten","."]
code: def store(self, *args):
      """Store a transition in replay memory.
  
      If the memory is full, the oldest one gets overwritten.
      """
      if not self._isfull():
          self._memory.append(None)
      position = self._next_position_then_increment()
      old_priority = (
          0
          if self._memory[position] is None
          else self._memory[position].priority
      )
      transition = _Transition(*args)
      self._memory[position] = transition
      if self._use_prioritized_replay:
          self._update_internal_nodes(
              position, transition.priority - old_priority
          )
  
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 2 for calling the ReplayMemory obj's underlying member method code with example usage: obj.store() without return types