Do you remember your first video game console? We remember ours. Decades ago, they provided hours of entertainment. Now, they’ve been repurposed to solve dynamic and stochastic optimization problems. With deep reinforcement learning techniques posting superhuman performance on a wide range of Atari games, we consider the problem of representing a classic logistics problem as a game. Then, we train agents to play it. We consider several game formats 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 heuristics, especially as problem size grows.