Multi-Objective Reinforcement Learning for Water Management
This work addresses the need for better benchmarks in MORL for researchers, but it is incremental as it applies existing methods to a new domain without major algorithmic innovations.
The paper tackled the lack of complex, realistic environments in multi-objective reinforcement learning (MORL) by introducing a water resource management case study for the Nile river basin and benchmarking existing MORL algorithms on it. The results showed that specialized water management methods outperformed state-of-the-art MORL approaches, highlighting scalability challenges in real-world scenarios.
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.