Gamifying the Vehicle Routing Problem with Stochastic Requests

Autor:	Kullman, Nicholas D., Dudorov, Nikita, Mendoza, Jorge E., Cousineau, Martin, Goodson, Justin C.
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Mathematics - Optimization and Control Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 problem size grows. Our work points to the representation of dynamic and stochastic optimization problems via games as a promising research direction. Comment: 21 pages, 9 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.05922 Zobrazit plný text záznamu View this record from Arxiv