MARCO：一种用于组合优化的记忆增强强化框架
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization
AI总结：
本文提出记忆增强强化框架 MARCO，通过共享记忆模块引导 NCO 搜索兼顾解质量与避免重复探索，在最大割、最大独立集和 TSP 上以低成本获得更优多样化解。
摘要：
神经组合优化（Neural Combinatorial Optimization，NCO）是一个新兴领域，它采用深度学习技术作为独立求解器来解决组合优化问题。尽管现有 NCO 方法具有潜力，但它们往往受到搜索空间探索效率低下的困扰，频繁导致陷入局部最优，或对先前访问过的状态进行冗余探索。本文介绍了一个通用框架，称为用于组合优化的记忆增强强化方法（Memory-Augmented Reinforcement for Combinatorial Optimization，MARCO），该框架可通过创新的记忆模块增强 NCO 中的构造式方法和改进式方法。MARCO 存储在整个优化轨迹中收集的数据，并在每个状态检索与上下文相关的信息。这样，搜索由两个相互竞争的准则引导：根据解的质量做出最佳决策，以及避免重新访问已经探索过的解。该方法促进了对可用优化预算的更高效利用。此外，得益于 NCO 模型的并行特性，多个搜索线程可以同时运行，并且所有线程共享同一个记忆模块，从而实现高效的协作探索。在最大割、最大独立集和旅行商问题上进行的实证评估表明，该记忆模块有效增强了探索，使模型能够发现多样化且质量更高的解。MARCO 以较低的计算成本取得了良好性能，为 NCO 领域确立了一个有前景的新方向。
英文摘要：
Neural Combinatorial Optimization (NCO) is an emerging domain where deep learning techniques are employed to address combinatorial optimization problems as a standalone solver. Despite their potential, existing NCO methods often suffer from inefficient search space exploration, frequently leading to local optima entrapment or redundant exploration of previously visited states. This paper introduces a versatile framework, referred to as Memory-Augmented Reinforcement for Combinatorial Optimization (MARCO), that can be used to enhance both constructive and improvement methods in NCO through an innovative memory module. MARCO stores data collected throughout the optimization trajectory and retrieves contextually relevant information at each state. This way, the search is guided by two competing criteria: making the best decision in terms of the quality of the solution and avoiding revisiting already explored solutions. This approach promotes a more efficient use of the available optimization budget. Moreover, thanks to the parallel nature of NCO models, several search threads can run simultaneously, all sharing the same memory module, enabling an efficient collaborative exploration. Empirical evaluations, carried out on the maximum cut, maximum independent set and travelling salesman problems, reveal that the memory module effectively increases the exploration, enabling the model to discover diverse, higher-quality solutions. MARCO achieves good performance in a low computational cost, establishing a promising new direction in the field of NCO.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
