COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space
Researchers have introduced COAgents, a cooperative multi-agent framework designed to efficiently solve complex Vehicle Routing Problems (VRP). By modeling the search process as a graph and separating search control from domain encoding, COAgents achieves state-of-the-art performance on challenging routing benchmarks.
Computer Science > Artificial Intelligence
Title:COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space
View PDF HTML (experimental)Abstract:Although Vehicle Routing Problems (VRP) are essential to many real-world systems, they remain computationally intractable at scale due to their combinatorial complexity. Traditional heuristics rely on handcrafted rules for local improvements and occasional \textit{jumps} to escape local minima, but often struggle to generalize across diverse instances. We introduce \textbf{COAgents}, a cooperative multi-agent framework that models the search process as a graph: nodes represent solutions, and edges correspond to either local refinements or large perturbations for diversification (i.e., jumps). A \textit{Partial Search Graph} (PSG) is dynamically constructed during search, enabling COAgents to train a Node Selection Agent and a Move Selection Agent to guide intensification, and a Jump Agent to trigger well-timed explorations of new regions. Unlike end-to-end learning approaches, COAgents cleanly separates problem-agnostic search control from compact domain-specific encoding, facilitating adaptability across tasks. Extensive experiments on the CVRP and VRPTW benchmarks show that COAgents remains competitive with several learn-to-search baselines on CVRP and sets a new state of the art among learning-based methods on the more challenging VRPTW instances, reducing the gap to the best-known solutions by 14% at $N!=!100$ and 44% at $N!=!50$ relative to the strongest neural solver (POMO), and by 21% and 40% respectively relative to ALNS.
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Source: arXiv cs.AI Recent
