Technical Challenges of Deploying Reinforcement Learning Agents for Game
  Testing in AAA Games
Jonas Gillberg Joakim Bergdahl Alessandro Sestini Andrew Eakins Linus Gisslen
Going from research to production, especially for large and complex software
systems, is fundamentally a hard problem. In large-scale game production, one
of the main reasons is that the development environment can be very different
from the final product. In this technical paper we describe an effort to add an
experimental reinforcement learning system to an existing automated game
testing solution based on scripted bots in order to increase its capacity. We
report on how this reinforcement learning system was integrated with the aim to
increase test coverage similar to [1] in a set of AAA games including
Battlefield 2042 and Dead Space (2023). The aim of this technical paper is to
show a use-case of leveraging reinforcement learning in game production and
cover some of the largest time sinks anyone who wants to make the same journey
for their game may encounter. Furthermore, to help the game industry to adopt
this technology faster, we propose a few research directions that we believe
will be valuable and necessary for making machine learning, and especially
reinforcement learning, an effective tool in game production.