Fairness in multi-agent reinforcement learning (MARL) is often framed as a workload balance problem, overlooking agent expertise and the structured coordination required in real-world domains. In healthcare, equitable task allocation requires workload balance or expertise alignment to prevent burnout and overuse of highly skilled agents. Workload balance refers to distributing an approximately equal number of subtasks or equalised effort across healthcare workers, regardless of their expertise. We make two contributions to address this problem. First, we propose FairSkillMARL, a framework that defines fairness as the dual objective of workload balance and skill-task alignment. Second, we introduce MARLHospital, a customizable healthcare-inspired environment for modeling team compositions and energy-constrained scheduling impacts on fairness, as no existing simulators are well-suited for this problem. We conducted experiments to compare FairSkillMARL in conjunction with four standard MARL methods, and against two state-of-the-art fairness metrics. Our results suggest that fairness based solely on equal workload might lead to task-skill mismatches and highlight the need for more robust metrics that capture skill-task misalignment. Our work provides tools and a foundation for studying fairness in heterogeneous multi-agent systems where aligning effort with expertise is critical.

翻译：多智能体强化学习（MARL）中的公平性常被简化为工作量平衡问题，忽视了智能体专业知识和现实领域所需的结构化协调。在医疗领域，公平的任务分配需要实现工作量平衡或技能对齐，以防止员工倦怠和过度使用高技能人员。工作量平衡指无论医疗工作者的专业水平如何，均分配近似相等的子任务数量或均衡的工作强度。针对此问题，我们做出两项贡献。首先，我们提出FairSkillMARL框架，将公平性定义为工作量平衡与技能-任务对齐的双重目标。其次，我们引入MARLHospital——一个可定制的医疗启发式仿真环境，用于建模团队构成及能量受限调度对公平性的影响，因为现有仿真器均不适用于此问题。我们通过实验将FairSkillMARL与四种标准MARL方法结合，并与两种先进公平性指标进行对比。结果表明，仅基于均等工作量的公平性可能导致任务-技能错配，并凸显了需要更鲁棒的指标来捕捉技能-任务失准现象。本研究为研究异构多智能体系统中的公平性提供了工具和基础，其中将工作投入与专业知识对齐至关重要。