Adaptive multi-agent formation control, which requires the formation to flexibly adjust along with the quantity variations of agents in a decentralized manner, belongs to one of the most challenging issues in multi-agent systems, especially under communication-limited constraints. In this paper, we propose a novel Consensus-based Decentralized Adaptive Formation (Cons-DecAF) framework. Specifically, we develop a novel multi-agent reinforcement learning method, Consensus-oriented Multi-Agent Communication (ConsMAC), to enable agents to perceive global information and establish the consensus from local states by effectively aggregating neighbor messages. Afterwards, we leverage policy distillation to accomplish the adaptive formation adjustment. Meanwhile, instead of pre-assigning specific positions of agents, we employ a displacement-based formation by Hausdorff distance to significantly improve the formation efficiency. The experimental results through extensive simulations validate that the proposed method has achieved outstanding performance in terms of both speed and stability.

翻译：自适应多智能体编队控制要求编队能以去中心化方式灵活适应智能体数量变化，是多智能体系统中最具挑战性的问题之一，尤其在通信受限条件下。本文提出一种新颖的基于共识的去中心化自适应编队框架（Cons-DecAF）。具体而言，我们开发了一种新型多智能体强化学习方法——共识导向多智能体通信（ConsMAC），使智能体能够通过有效聚合邻居消息感知全局信息，并从局部状态建立共识。随后，利用策略蒸馏实现自适应编队调整。同时，我们摒弃了预分配智能体特定位置的方式，采用基于Hausdorff距离的位移编队显著提升编队效率。通过大量仿真实验，验证了所提方法在速度与稳定性方面均取得了卓越性能。