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.

提出了一种新的 Cons-DecAF 框架，通过使用 ConsMAC 方法进行多智能体强化学习，利用策略蒸馏实现自适应编队调整，并采用基于 Hausdorff 距离的位移式编队，取得了出色的速度和稳定性表现。