• Measuring the impact of cooperative rewards on AI

      Harmon, Dain; Lawlor, Orion S.; Chappell, Glenn G.; Metzgar, Jonathan B. (2020-12)
      We consider the effects of varying individualistic and team rewards on learning for a Deep Q-Network AI in a multi-agent system, using a synthetic team game ‘Futlol’ designed for this purpose. Experimental results with this game using the OpenSpiel framework indicate that mixed reward structures result in lower win rates. It is unclear if this is due to faster learning on simpler reward structures or a flaw in the nature of the reward system.