A Unified Framework for Locality in Scalable MARL 事件

A Unified Framework for Locality in Scalable MARL arXiv:2602.16966v2 Announce Type: replace-cross Abstract: Scalable methods for networked multi-agent reinforcement learning let each agent plan using only a small neighborhood of the agent graph. This works only when the system is value-local, meaning a perturbation at one agent affects the long-run value at another agent weakly when the two are far apart. In the average-reward setting, the standard way to certify locality is the Dobrushin row-s