@techreport{TR-IC-PFG-18-02,
   number = {IC-PFG-18-02},
   author = {Lucas {Mageste de Almeida} and Esther {Colombini}},
   title = {{Playing NES through the use of a DDQN}},
   month = {July},
   year = {2018},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 21 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       This  work  uses the deep learning model known as DDQN to learn
       control policies for a variety of NES games. The input given to
       the model are raw pixels of the game screen and the output is a
       value  function estimating future rewards. The learning process
       uses  reinforcement  learning  exclusively  and the same set of
       hyper-parameters for every game, with no adjustments. This work
       provides  initial  insight  into  RL  used  for  NES  games and
       provides  full-support  for  the training of any other NES game
       using DDQN as its underlying learning agent.
  }
}