Predicting Game Difficulty and Churn Without Players
  We propose a novel simulation model that is able to predict the per-level
churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play
game. Our primary contribution is to combine AI gameplay using Deep
Reinforcement Learning (DRL) with a simulation of how the player population
evolves over the levels. The AI players predict level difficulty, which is used
to drive a player population model with simulated skill, persistence, and
boredom. This allows us to model, e.g., how less persistent and skilled players
are more sensitive to high difficulty, and how such players churn early, which
makes the player population and the relation between difficulty and churn
evolve level by level. Our work demonstrates that player behavior predictions
produced by DRL gameplay can be significantly improved by even a very simple
population-level simulation of individual player differences, without requiring
costly retraining of agents or collecting new DRL gameplay data for each
simulated player.
