Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications. However, achieving efficient communication among agents has always been an overarching problem in MARL. In this work, we propose Variance Based Control (VBC), a simple yet efficient technique to improve communication efficiency in MARL. By limiting the variance of the exchanged messages between agents during the training phase, the noisy component in the messages can be eliminated effectively, while the useful part can be preserved and utilized by the agents for better performance. Our evaluation using a challenging set of StarCraft II benchmarks indicates that our method achieves $2-10\times$ lower in communication overhead than state-of-the-art MARL algorithms, while allowing agents to better collaborate by developing sophisticated strategies.

本文提出基于方差控制的多智能体强化学习技术（Variance Based Control，VBC），通过控制不同agent之间交流的消息方差来提高通信效率，可使agent利用有用信息，减少噪音影响，从而在StarCraft II游戏中实现更好的合作策略和低延迟通信。

基于方差控制的多智能体强化学习高效通信