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

Article type: Research Article

Authors: Mitropoulos, Konstantinos^* | Vasilopoulos, Georgios | Giagtzoglou, Kyriakos | Kalles, Dimitrios

Affiliations: School of Science and Technology, Hellenic Open University, Patras, Greece

Correspondence: [*] Corresponding author: Konstantinos Mitropoulos, School of Science and Technology, Hellenic Open University, Patras, Greece. E-mail: [email protected].

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.

Keywords: Artificial intelligence, machine learning, reinforcement learning, software engineering

DOI: 10.3233/IDT-210212

Journal: Intelligent Decision Technologies, vol. 16, no. 2, pp. 431-439, 2022