Many real-world problems (e.g., resource management, autonomous driving, drug discovery) require optimizing multiple, conflicting objectives. Multi-objective reinforcement learning (MORL) extends classic reinforcement learning to handle multiple objectives simultaneously, yielding a set of policies that capture various trade-offs. However, the MORL field lacks complex, realistic environments and benchmarks. We introduce a water resource (Nile river basin) management case study and model it as a MORL environment. We then benchmark existing MORL algorithms on this task. Our results show that specialized water management methods outperform state-of-the-art MORL approaches, underscoring the scalability challenges MORL algorithms face in real-world scenarios.

翻译：许多现实世界问题（例如资源管理、自动驾驶、药物发现）需要同时优化多个相互冲突的目标。多目标强化学习（MORL）将经典强化学习扩展至同时处理多个目标，生成一系列捕捉不同权衡策略的解决方案。然而，MORL领域目前缺乏复杂且真实的环境与基准测试。本研究引入尼罗河流域水资源管理案例，并将其建模为MORL环境，随后在此任务上对现有MORL算法进行基准评估。结果表明，专业的水资源管理方法优于当前最先进的MORL方法，这突显了MORL算法在现实场景中面临的可扩展性挑战。