论文标题
多代理拾取和交付问题：MAPF，MARL及其仓库应用程序
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications
论文作者
论文摘要
我们研究了基于不同原理的多代理拾取和交付问题（MAPD）问题的两种最先进的解决方案 - 多试路径找到（MAPF）和多机构增强学习（MARL）。具体而言，研究了一种最近的MAPF算法，称为基于冲突的搜索（CBS）和当前称为共享经验参与者 - 批评（SAC）的MARL算法。尽管这些算法的性能是在其单独的工作线中使用完全不同的指标测量的，但我们的目标是在模拟的仓库自动化环境中全面基准这两种方法。
We study two state-of-the-art solutions to the multi-agent pickup and delivery (MAPD) problem based on different principles -- multi-agent path-finding (MAPF) and multi-agent reinforcement learning (MARL). Specifically, a recent MAPF algorithm called conflict-based search (CBS) and a current MARL algorithm called shared experience actor-critic (SEAC) are studied. While the performance of these algorithms is measured using quite different metrics in their separate lines of work, we aim to benchmark these two methods comprehensively in a simulated warehouse automation environment.
