An experimental comparison of single-agent and multi-agent learning-and-playing algorithms in a gaming ecosystem

Abstract

The first version of the RLGame ecosystem, which features a collection of conventional Artificial Intelligence and Reinforcement Learning techniques for learning to play (and, actually, playing) a board game, was adopted for the development of the second version that supports Multiagent Reinforcement Learning and a sequence of games. We present an experimental comparison of a variety of algorithms which are available within the ecosystem and comment on the potential to investigate single-player vs multi-player scenarios, alongside some experimental results which suggest that loosely co-ordinated game play can be superior to fully co-ordinated game play.