Gamifying the Vehicle Routing Problem with Stochastic Requests
This work addresses dynamic and stochastic optimization problems in logistics by gamifying them, offering a novel approach that could improve efficiency in routing tasks.
The authors tackled the vehicle routing problem with stochastic requests by representing it as a video game and training deep reinforcement learning agents to solve it, showing that with optimal game design, these agents outperform optimization-based benchmarks, particularly as problem size increases.
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.