@techreport{TR-IC-PFG-17-18,
   number = {IC-PFG-17-18},
   author = {Renato Landim Vargas and Esther Luna Colombini},
   title = {{Atari-playing Robot}},
   month = {December},
   year = {2017},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 14 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Great  results  achieved  by  Deep  Q-Networks  on  Atari games
       gathered  a lot of attention from researchers in the past year.
       Despite  this,  not  many  research has been conducted on using
       these   networks   on   real-world   robots   learning  through 
       high-dimensional  sensory  input. This work proposes ALE-Robot,
       which  is  an  architecture  that  uses  ALE  (Arcade  Learning 
       Environment)  and  the  V-REP  environment  to simulate a robot
       learning  to  play  Atari  directly  from its camera and a game
       controller   through   Reinforcement   Learning.  Results  have 
       demonstrated that, when applied to real robotics architectures,
       longer  training  periods  and re-shaping rewards functions are
       required.  Moreover,  more  time  and  computational  power  is 
       necessary to test the feasibility of the proposed approach over
       different hyperparameters.
  }
}