This paper describes an Ensemble Agent for the classic arcade game Ms. Pac-Man. Our approach decomposes the problem into sub-goals. An expert agent is created for each sub-goal, with all experts reporting to a central arbiter. Our Ensemble Agent has achieved the AI world record for the arcade version of Ms. Pac-Man with a score of 162,280. For comparison, a MCTS-based monolithic agent was also created, based on the same accurate forward model that the Ensemble Agent uses, reaching a score of 115,180.
|Title of host publication||2018 IEEE Conference on Computational Intelligence and Games (CIG)|
|Number of pages||8|
|Publication status||Published - 15 Oct 2018|