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.

翻译：从研究过渡到生产，尤其是针对大型复杂软件系统，本质上是一个难题。在大规模游戏生产中，主要原因之一是开发环境可能与最终产品存在巨大差异。本技术论文描述了我们为现有基于脚本化机器人的自动化游戏测试解决方案添加实验性强化学习系统以提升其能力的努力。我们报告了该强化学习系统如何被集成，旨在增加类似[1]中对《战地2042》和《死亡空间》（2023）等AAA游戏的测试覆盖率。本技术论文旨在展示在游戏生产中利用强化学习的用例，并涵盖任何希望为其游戏走同样道路的人可能遇到的重大时间消耗点。此外，为帮助游戏行业更快采用该技术，我们提出了一些我们认为有价值且必要的研究方向，以使机器学习，尤其是强化学习，成为游戏生产中的有效工具。