Combinatorial optimization problems involving multiple agents are notoriously challenging due to their NP-hard nature and the necessity for effective agent coordination. Despite advancements in learning-based methods, existing approaches often face critical limitations, including suboptimal agent coordination, poor generalization, and high computational latency. To address these issues, we propose PARCO (Parallel AutoRegressive Combinatorial Optimization), a general reinforcement learning framework designed to construct high-quality solutions for multi-agent combinatorial tasks efficiently. To this end, PARCO integrates three key novel components: (1) transformer-based communication layers to enable effective agent collaboration during parallel solution construction, (2) a multiple pointer mechanism for low-latency, parallel agent decision-making, and (3) priority-based conflict handlers to resolve decision conflicts via learned priorities. We evaluate PARCO in multi-agent vehicle routing and scheduling problems, where our approach outperforms state-of-the-art learning methods, demonstrating strong generalization ability and remarkable computational efficiency. We make our source code publicly available to foster future research: https://github.com/ai4co/parco.

翻译：多智能体组合优化问题因其NP难特性及对智能体间有效协同的需求而极具挑战性。尽管基于学习的方法已取得进展，现有方案仍常面临关键局限，包括智能体协同效果欠佳、泛化能力不足以及计算延迟过高。为应对这些问题，我们提出PARCO（并行自回归组合优化框架），这是一个通用的强化学习框架，旨在高效构建多智能体组合任务的高质量解。为此，PARCO集成了三个关键创新模块：(1) 基于Transformer的通信层，以支持并行解构建过程中的有效智能体协作；(2) 多指针机制，实现低延迟的并行智能体决策；(3) 基于优先级的冲突处理器，通过习得的优先级解决决策冲突。我们在多智能体车辆路径规划与调度问题中评估PARCO，结果表明该方法优于当前最先进的学习方法，展现出强大的泛化能力与显著的计算效率。我们公开了源代码以促进后续研究：https://github.com/ai4co/parco。