Predicting Game Difficulty and Churn Without Players

TL;DR

This paper introduces a simulation model combining Deep Reinforcement Learning gameplay with player population dynamics to predict level difficulty and churn in a mobile game, enhancing understanding of player behavior without extensive retraining.

Contribution

It presents a novel approach integrating AI gameplay with population simulation to predict player churn and difficulty evolution in mobile games.

Findings

DRL-based predictions improve with simple population modeling.

Player skill, persistence, and boredom influence churn and difficulty.

Model accurately reflects how player behavior impacts game progression.

Abstract

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.