class_docstr: DDPG class
class_docstr_tok: ["DDPG","class"]
docstr: Store data in data buffer
  :param s: state
  :param a: act
  :param r: reward
  :param s_: next state
  :return: None
docstr_tok: ["Store","data","in","data","buffer",":","param","s",":","state",":","param","a",":","act",":","param","r",":","reward",":","param","s_",":","next","state",":","return",":","None"]
code: def store_transition(self, s, a, r, s_):
      """
      Store data in data buffer
      :param s: state
      :param a: act
      :param r: reward
      :param s_: next state
      :return: None
      """
      s = s.astype(np.float32)
      s_ = s_.astype(np.float32)
      transition = np.hstack((s, a, [r], s_))
      index = (
          self.pointer % MEMORY_CAPACITY
      )  # replace the old memory with new memory
      self.memory[index, :] = transition
      self.pointer += 1
  
file_path: TensorLayer/examples/reinforcement_learning/tutorial_DDPG.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 5 for calling the DDPG obj's underlying member method code with example usage: obj.store_transition(s, a, r, s_) without return types