Gamifying the Vehicle Routing Problem with Stochastic Requests
Nicholas D. Kullman, Nikita Dudorov, Jorge E. Mendoza, Martin Cousineau, Justin C. Goodson
TL;DR
This work considers the task of representing a classic logistics problem as a game, and considers several game designs for the vehicle routing problem with stochastic requests, showing how various design features impact agents’ performance, including perspective, field of view, and minimaps.
Abstract
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.