class_docstr: PG class
docstr: store data in memory buffer
  :param s: state
  :param a: act
  :param r: reward
  :return:
docstr_tok: ["store","data","in","memory","buffer",":","param","s",":","state",":","param","a",":","act",":","param","r",":","reward",":","return",":"]
code: def store_transition(self, s, a, r):
      """
      store data in memory buffer
      :param s: state
      :param a: act
      :param r: reward
      :return:
      """
      self.state_buffer.append(np.array([s], np.float32))
      self.action_buffer.append(a)
      self.reward_buffer.append(r)
  
file_path: TensorLayer/examples/reinforcement_learning/tutorial_PG.py
imports: import argparse
  import os
  import time
  import gym
  import matplotlib.pyplot
  import numpy
  import tensorflow
  import tensorlayer
label_desc: Use image node_id 3 for calling the PolicyGradient obj's underlying member method code with example usage: obj.store_transition(s, a, r) without return types