用尼罗河流域管理测试多目标强化学习，发现现有算法在真实场景中表现不佳。
Multi-Objective Reinforcement Learning for Water Management
- 将尼罗河水资源管理建模为多目标强化学习环境
- 专用管理方法优于当前最先进的多目标强化学习算法
- 揭示了多目标算法在真实世界应用中的可扩展性瓶颈
许多现实问题（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）扩展了经典强化学习，能够同时处理多个目标，生成一组反映不同权衡的策略。然而，该领域缺乏复杂且真实的环境与基准。本文引入尼罗河流域水资源管理案例，并将其建模为MORL环境，对现有MORL算法在此任务上进行基准测试。结果表明，专门针对水资源管理的方法优于当前最先进的MORL方法，凸显了MORL算法在真实场景中面临的可扩展性挑战。
原文摘要 · Abstract (English)
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.
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