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.

翻译：本文探讨了最优传输（Optimal Transport, OT）理论与多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）的融合。该融合利用OT处理分布与运输问题的能力，以提升MARL的效率、协同性和适应性。OT对MARL产生影响的五个关键领域包括：(1) 策略对齐——利用OT的Wasserstein距离将分歧的智能体策略统一至共同目标；(2) 分布式资源管理——通过OT优化智能体间的资源分配；(3) 处理非平稳性——借助OT适应动态环境变化；(4) 可扩展多智能体学习——利用OT将大规模学习目标分解为可管理任务；(5) 提升能效——应用OT原理开发可持续的MARL系统。本文阐明了OT与MARL的协同如何解决规模扩展问题、优化资源分布、在协作环境中对齐智能体策略，并确保在动态变化条件下的适应性。