class_docstr: DDPG class
class_docstr_tok: ["DDPG","class"]
docstr: Choose action
  :param s: state
  :param greedy: get action greedy or not
  :return: act
docstr_tok: ["Choose","action",":","param","s",":","state",":","param","greedy",":","get","action","greedy","or","not",":","return",":","act"]
code: def get_action(self, s, greedy=False):
      """
      Choose action
      :param s: state
      :param greedy: get action greedy or not
      :return: act
      """
      a = self.actor(np.array([s], dtype=np.float32))[0]
      if greedy:
          return a
      return np.clip(
          np.random.normal(a, self.var),
          -self.action_range,
          self.action_range,
      )
  
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 3 for calling the DDPG obj's underlying member method code with example usage: obj.get_action(s, greedy) and returns: np, a