ARMATA: Auto-Regressive Multi-Agent Task Assignment
This work addresses the complex problem of coordinating multi-agent systems over spatially distributed areas, providing a significant performance gain over existing methods for practitioners in logistics, robotics, and related fields.
ARMATA proposes a centralized end-to-end auto-regressive framework for multi-agent task assignment that jointly handles area allocation and routing, achieving up to 20% improvement in solution quality over industrial solvers like Google OR-Tools and IBM CPLEX while reducing computation time from hours to seconds.
Coordinating multi-agent systems over spatially distributed areas requires solving a complex hierarchical problem: first distributing areas among agents (allocation) and subsequently determining the optimal visitation order (routing). Existing methods typically decouple these stages ignoring inter-stage dependencies or rely on decentralized heuristics that lack global context. In this work, we propose a centralized, fully end-to-end auto-regressive framework that jointly generates allocation decisions and routing sequences. The core contribution of our approach is a multi-stage decoding mechanism that unifies high-level allocation and low-level routing in a single autoregressive pass while maintaining a centralized global state. This enables the model to implicitly balance workload distribution with routing efficiency, avoiding local optima common in decentralized methods. Extensive experiments demonstrate that our method significantly outperforms diverse baselines, achieving up to a 20\% improvement in solution quality over industrial solvers such as Google OR-Tools, IBM CPLEX, and LKH-3, while reducing computation time from hours to seconds.