docstr: Keep a running estimate of a quantity. This is a bit like mean
  but more sensitive to recent changes.
  
  Parameters
  ----------
  gamma: float
      Must be between 0 and 1, where 0 is the most sensitive to recent
      changes.
  init_value: float or None
      Initial value of the estimate. If None, it will be set on the first update.
docstr_tok: ["Keep","a","running","estimate","of","a","quantity",".","This","is","a","bit","like","mean","but","more","sensitive","to","recent","changes",".","Parameters","--","--","--","--","--","gamma",":","float","Must","be","between","0","and","1",",","where","0","is","the","most","sensitive","to","recent","changes",".","init_value",":","float","or","None","Initial","value","of","the","estimate",".","If","None",",","it","will","be","set","on","the","first","update","."]
code: def __init__(self, gamma, init_value=None):
      """Keep a running estimate of a quantity. This is a bit like mean
      but more sensitive to recent changes.
  
      Parameters
      ----------
      gamma: float
          Must be between 0 and 1, where 0 is the most sensitive to recent
          changes.
      init_value: float or None
          Initial value of the estimate. If None, it will be set on the first update.
      """
      self._value = init_value
      self._gamma = gamma
  
imports: import gym
  import numpy
  import os
  import pickle
  import random
  import tempfile
  import zipfile
label_desc: Use image node_id 1 to create a new RunningAvg object from inherited base classes: object with example: obj = RunningAvg(gamma, init_value)