  This paper explores the integration of optimal transport (OT) theory with
multi-agent reinforcement learning (MARL). This integration uses OT to handle
distributions and transportation problems to enhance the efficiency,
coordination, and adaptability of MARL. There are five key areas where OT can
impact MARL: (1) policy alignment, where OT's Wasserstein metric is used to
align divergent agent strategies towards unified goals; (2) distributed
resource management, employing OT to optimize resource allocation among agents;
(3) addressing non-stationarity, using OT to adapt to dynamic environmental
shifts; (4) scalable multi-agent learning, harnessing OT for decomposing
large-scale learning objectives into manageable tasks; and (5) enhancing energy
efficiency, applying OT principles to develop sustainable MARL systems. This
paper articulates how the synergy between OT and MARL can address scalability
issues, optimize resource distribution, align agent policies in cooperative
environments, and ensure adaptability in dynamically changing conditions.
