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.

When Rovio Entertainment released Angry Birds in 2009, mobile games weren't really a thing -- the Apple App Store, after all, had only launched a year earlier. Once the game went viral, however, Rovio saw its future was in the cloud. "It was an unchartered area for most of games," Rovio CTO Petri Hyökyranta said to ZDNet. Rovio signed up for the relatively young Amazon Web Services in 2011 and got to work building Beacon, a service platform for all of Rovio's games. Today, after 10 years on the market, Angry Birds games are still played by millions of people around the world each day.