  Different from what happens for most types of software systems, testing video
games has largely remained a manual activity performed by human testers. This
is mostly due to the continuous and intelligent user interaction video games
require. Recently, reinforcement learning (RL) has been exploited to partially
automate functional testing. RL enables training smart agents that can even
achieve super-human performance in playing games, thus being suitable to
explore them looking for bugs. We investigate the possibility of using RL for
load testing video games. Indeed, the goal of game testing is not only to
identify functional bugs, but also to examine the game's performance, such as
its ability to avoid lags and keep a minimum number of frames per second (FPS)
when high-demanding 3D scenes are shown on screen. We define a methodology
employing RL to train an agent able to play the game as a human while also
trying to identify areas of the game resulting in a drop of FPS. We demonstrate
the feasibility of our approach on three games. Two of them are used as
proof-of-concept, by injecting artificial performance bugs. The third one is an
open-source 3D game that we load test using the trained agent showing its
potential to identify areas of the game resulting in lower FPS.
