Benchmark Instances for Multi-Item Retrieval

该数据集包含了一系列为不同问题设置生成的基准实例，涵盖了单件物品检索和多件物品检索问题。这些实例用于评估强化学习方法的性能，并将其与现有算法进行比较。该数据集共包含10,835个实例，分为四个系列，用于不同目的，包括测试强化学习方法解决方案的质量以及对小规模实例进行敏感性分析。这些实例的规模分为小型、中型和大型，任务主要集中在基于谜题的存储系统中的多件物品检索。

This dataset comprises a set of benchmark instances generated for various problem settings, covering single-item retrieval and multi-item retrieval problems. These instances are used to evaluate the performance of reinforcement learning methods and compare them with existing algorithms. This dataset contains a total of 10,835 instances, divided into four series for different purposes, including testing the solution quality of reinforcement learning methods and conducting sensitivity analysis on small-scale instances. The scale of these instances is categorized into small, medium, and large sizes, and the tasks primarily focus on multi-item retrieval in puzzle-based storage systems.