Fairness-Driven Explainable Learning in Multi-Agent Reinforcement Learning

Abstract

Fairness and explainability are important issues that must be addressed in multi-agent reinforcement learning (MARL) systems. In this study, we propose a novel approach that directly incorporates fairness constraints and layer-wise relevance propagation (LRP) into multi-agent training. Through the proposed method, explainability and fairness can be addressed simultaneously, improving the interpretability of agent’s decisions and guaranteeing that agents are assigned tasks equitably. We evaluate the performance of the proposed method based on a resource allocation problem. The results show average fairness and explainability ratings of 0.921 and 0.931, respectively. Preliminary results show that this strategy greatly enhances system fairness and explainability while maintaining a competitive average system reward. Furthermore, by encouraging efficient resource use, the proposed method advances the principles of green artificial intelligence.