用分步抽象简化多智能体协同决策，提升效率与可扩展性
An Efficient Approach for Cooperative Multi-Agent Learning Problems
- 引入监督者元智能体，将联合动作序列化处理
- 在多种环境上成功协调不同规模的多智能体
- 适合需要高效协同的复杂多智能体系统设计
本文提出一种中心化多智能体学习框架，用于学习多个需协同完成任务的智能体的同步行为策略。传统中心化方法常因所有个体动作组合构成的联合动作空间爆炸而受限。本方法通过顺序抽象机制，引入名为'监督者'的元智能体，将联合动作抽象为对各智能体的动作逐次分配。该顺序抽象不仅缩小了中心化联合动作空间，还显著提升了框架的可扩展性与效率。实验表明，该方法在多种不同规模的多智能体学习环境中均能有效实现智能体间的协调。
原文摘要 · Abstract (English)
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.
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