Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication
This addresses a challenging problem in multi-agent systems for applications requiring flexible, decentralized coordination, but it appears incremental as it builds on existing consensus and reinforcement learning methods.
The paper tackles adaptive multi-agent formation control under communication-limited constraints by proposing a Consensus-based Decentralized Adaptive Formation (Cons-DecAF) framework, which achieves outstanding performance in speed and stability through simulations.
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.