Can MARL optimize sustainable maritime logistics?
CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics
This paper introduces CH-MARL, a hierarchical multi-agent reinforcement learning system for optimizing maritime shipping routes while minimizing fuel consumption and emissions under real-world constraints like port capacity. Agents negotiate routes and resource allocation considering fairness (so smaller ships aren't disadvantaged).
Relevant to LLM-based multi-agent systems are CH-MARL's hierarchical structure (strategic and operational agents), its dynamic constraint enforcement (via primal-dual methods which could be adapted for LLM prompt constraints), and its focus on fairness in resource allocation. These concepts offer a path to scalable, constraint-aware, and ethical multi-agent systems potentially leveraging LLMs as agents.