Dynamic difficulty adjustment ($DDA$) is a process of automatically changing a game difficulty for the optimization of user experience. It is a vital part of almost any modern game. Most existing DDA approaches concentrate on the experience of a player without looking at the rest of the players. We propose a method that automatically optimizes user experience while taking into consideration other players and macro constraints imposed by the game. The method is based on deep neural network architecture that involves a count loss constraint that has zero gradients in most of its support. We suggest a method to optimize this loss function and provide theoretical analysis for its performance. Finally, we provide empirical results of an internal experiment that was done on $200,000$ players and was found to outperform the corresponding manual heuristics crafted by game design experts.

翻译：动态困难调整(DDA$)是一个自动改变游戏困难以优化用户经验的过程,几乎是任何现代游戏的关键部分。大多数现有的DDA方法都集中在玩家的经验上,而不看其他玩家。我们提出了一个方法,在考虑其他玩家和游戏造成的宏观限制的同时,自动优化用户经验。这个方法基于一个深度的神经网络结构,它涉及计算损失限制,其大部分支持是零梯度。我们建议了一种方法来优化这一损失功能,并提供理论分析。最后,我们提供了对20万美元玩家进行的内部实验的经验结果,并发现它比游戏设计专家所设计的相应手动重力要强。