class_docstr: Write me
      
class_docstr_tok: ["Write","me"]
code: def select_action(self, state):
      batch = self.process_state_batch([state])
      action = self.actor.predict_on_batch(batch).flatten()
      assert action.shape == (self.nb_actions,)
  
      # Apply noise, if a random process is set.
      if self.training and self.random_process is not None:
          noise = self.random_process.sample()
          assert noise.shape == action.shape
          action += noise
  
      return action
  
imports: from __future__ import division
  from collections import deque
  import os
  import warnings
  import numpy
  import keras.backend
  import keras.optimizers
  from rl.core import Agent
  from rl.random import OrnsteinUhlenbeckProcess
  from rl.util import *
label_desc: Use image node_id 9 for calling the DDPGAgent obj's underlying member method code with example usage: obj.select_action(state) and returns: action