An Efficient Approach for Cooperative Multi-Agent Learning Problems
This addresses coordination challenges in multi-agent systems, offering a scalable solution for tasks requiring agent collaboration, though it appears incremental in its approach.
The paper tackles the scalability problem in centralized multi-agent learning by introducing a sequential abstraction with a supervisor meta-agent, which simplifies the joint action space and improves efficiency, as demonstrated in experiments across various environments.
In this article, we propose a centralized Multi-Agent Learning framework for learning a policy that models the simultaneous behavior of multiple agents that need to coordinate to solve a certain task. Centralized approaches often suffer from the explosion of an action space that is defined by all possible combinations of individual actions, known as joint actions. Our approach addresses the coordination problem via a sequential abstraction, which overcomes the scalability problems typical to centralized methods. It introduces a meta-agent, called \textit{supervisor}, which abstracts joint actions as sequential assignments of actions to each agent. This sequential abstraction not only simplifies the centralized joint action space but also enhances the framework's scalability and efficiency. Our experimental results demonstrate that the proposed approach successfully coordinates agents across a variety of Multi-Agent Learning environments of diverse sizes.