class_docstr: DiscreteQFArgmax policy.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      qf (garage.q_functions.QFunction): The q-function used.
      name (str): Name of the policy.
docstr: Get action from this policy for the input observation.
  
  Args:
      observation (numpy.ndarray): Observation from environment.
  
  Returns:
      numpy.ndarray: Single optimal action from this policy.
      dict: Predicted action and agent information. It returns an empty
          dict since there is no parameterization.
docstr_tok: ["Get","action","from","this","policy","for","the","input","observation",".","Args",":","observation","(","numpy.ndarray",")",":","Observation","from","environment",".","Returns",":","numpy.ndarray",":","Single","optimal","action","from","this","policy",".","dict",":","Predicted","action","and","agent","information",".","It","returns","an","empty","dict","since","there","is","no","parameterization","."]
code: def get_action(self, observation):
      """Get action from this policy for the input observation.
  
      Args:
          observation (numpy.ndarray): Observation from environment.
  
      Returns:
          numpy.ndarray: Single optimal action from this policy.
          dict: Predicted action and agent information. It returns an empty
              dict since there is no parameterization.
  
      """
      opt_actions, agent_infos = self.get_actions([observation])
      return opt_actions[0], {k: v[0] for k, v in agent_infos.items()}
  
imports: import akro
  import numpy
  import tensorflow
  from garage.tf.models import Module
  from garage.tf.policies.policy import Policy
label_desc: Use image node_id 3 for calling the DiscreteQFArgmaxPolicy obj's underlying member method code with example usage: obj.get_action(observation) and returns: opt_actions,  unknown