DL-DDA - Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints

Dynamic difficulty adjustment (DDA) is a process of automatically changing a game difficulty for optimization of the 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 the user experience while taking into consideration other players and the macro constraints imposed by the game. The method is based on a 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 of 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.