Impact of Heterogeneity on Multi-Agent Reinforcement Learning - Marques, Rodrigo Fonseca; Patrocínio Júnior, Zenilton Kleber Gonçalves do (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2023: Anais do XX Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2023.234582, 2023)
Most Multi-Agent Reinforcement Learning (MARL) methods and studies use homogenous agents. The majority of study on heterogeneity concentrates on agents with different skill sets. However, in real-world applications, agents frequently possess the same set of skills but different degrees. In this paper, we propose a novel model for heterogeneous agents in a MARL system, in which they share a standard skill set but have different degrees of intensity. Experiments were carried out in the framework of Soccer Twos, a competitive and cooperative game, and also with Tennis, which has competitive gameplay. Results demonstrate that heterogeneous agents perform better than homogeneous ones in both environments and also acquire organizational abilities in Soccer Twos.