The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning

TL;DR

This work investigates injecting optimal transport (OT) into multi-agent reinforcement learning (MARL) to tackle coordination, resource sharing, and adaptability in dynamic environments. By leveraging the Wasserstein distance $W_p$, the authors outline a five-fold integration: policy alignment, distributed resource management, non-stationarity handling, scalable learning, and energy-aware designs. The approach presents concrete OT-based formulations for policy and resource alignment, adaptive learning rates under distribution shifts, and hierarchical scalability, aiming to produce coherent, efficient, and robust MARL systems. If realized, these OT-enabled MARL strategies could significantly improve performance in large-scale, energy-constrained, and rapidly changing domains.

Abstract

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.