  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.
