Evolving FPS Game Players by Using Continuous EDA-RL
Hajime Tsubota, Hisashi Handa · Hiroshima University Acedemic Information Repository (Hiroshima University) · 2009
This paper extends EDA-RL, Estimation of Distribution Algorithms for Reinforcement Learning Problems, to continuous domain. The extended EDA-RL is used to constitiute FPS game players. In order to cope with continuous input-output relations, Gaussian Network is employed as in EBNA. Simulation results on Unreal Tournament 2004, one of major FPS games, confirm the effectiveness of the proposed method.