Applied Soft Computing – A population-based approach for multi-agent interpretable reinforcement learning


Multi-Agent Reinforcement Learning (MARL) made significant progress in the last decade, mainly thanks to the major developments in the field of Deep Neural Networks (DNNs). However, DNNs suffer from a fundamental issue: their lack of interpretability. While this is true for most applications of DNNs, this is exacerbated in their applications in MARL. In fact, the mutual interactions between agents and environment, as well as across agents, make it particularly difficult to understand learned strategies in these settings.