Gamifying the Vehicle Routing Problem with Stochastic Requests
将随机需求的车辆路径问题设计成Atari游戏，用深度强化学习训练智能体求解，发现合适的游戏设计能让通用Atari智能体在问题规模增大时超越传统优化方法。
Do you remember your first video game console? We remember ours. Decades ago, they provided hours of entertainment. Now, we have repurposed them to solve dynamic and stochastic optimization problems. With deep reinforcement learning methods posting superhuman performance on a wide range of Atari games, we consider the task of representing a classic logistics problem as a game. Then, we train agents to play it. We consider several game designs for the vehicle routing problem with stochastic requests. We show how various design features impact agents’ performance, including perspective, field of view, and minimaps. With the right game design, general purpose Atari agents outperform optimization-based benchmarks, especially as the problem size grows. Our work points to the representation of dynamic and stochastic optimization problems via games as a promising research direction. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This research was enabled in part by support from Calcul Québec, the Digital Research Alliance of Canada, HEC Montreal, and the Institute for Data Valorization (IVADO). Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0838 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0838 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .