class_docstr: Implement the greedy policy
  
  Greedy policy returns the current best action according to q_values
class_docstr_tok: ["Implement","the","greedy","policy","Greedy","policy","returns","the","current","best","action","according","to","q_values"]
docstr: Return the selected action
  
  # Arguments
      q_values (np.ndarray): List of the estimations of Q for each action
  
  # Returns
      Selection action
code: def select_action(self, q_values):
      """Return the selected action
  
      # Arguments
          q_values (np.ndarray): List of the estimations of Q for each action
  
      # Returns
          Selection action
      """
      assert q_values.ndim == 1
      action = np.argmax(q_values)
      return action
  
imports: from __future__ import division
  import numpy
  from rl.util import *
label_desc: Use image node_id 1 for calling the GreedyQPolicy obj's underlying member method code with example usage: obj.select_action(q_values) and returns: action