# Predicting Game Engagement and Difficulty Using AI Players

arXiv: https://arxiv.org/abs/2107.12061 · HTML: https://mlsanity.com/paper/2107.12061/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 70 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Augmenting Automated Game Testing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.15819/) (2021)
- [Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics](https://mlsanity.com/paper/1802.06881/) (2018)
- [Automated Video Game Testing Using Synthetic and Human-Like Agents](https://mlsanity.com/paper/1906.00317/) (2019)
- [Predicting Game Difficulty and Churn Without Players](https://mlsanity.com/paper/2008.12937/) (2020)
- [Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing](https://mlsanity.com/paper/2003.07813/) (2020)
- [Inferring Cognitive Models from Data using Approximate Bayesian Computation](https://mlsanity.com/paper/1612.00653/) (2016)
- [Metagame Autobalancing for Competitive Multiplayer Games](https://mlsanity.com/paper/2006.04419/) (2020)
- [Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability](https://mlsanity.com/paper/1905.10998/) (2019)

## Built on by (most cited first, 5 of 5)

- [CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning](https://mlsanity.com/paper/2203.11096/) (2022)
- [Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors](https://mlsanity.com/paper/2210.02506/) (2022)
- [RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games](https://mlsanity.com/paper/2307.01676/) (2023)
- [Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data](https://mlsanity.com/paper/2401.17436/) (2024)
- [Robo-Saber: Generating and Simulating Virtual Reality Players](https://mlsanity.com/paper/2602.18319/) (2026)

## Abstract (arXiv)

This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. It has previously been demonstrated that Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty and player engagement, operationalized as average pass and churn rates. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS). We also motivate an enhanced selection strategy for predictor features, based on the observation that an AI agent's best-case performance can yield stronger correlations with human data than the agent's average performance. Both additions consistently improve the prediction accuracy, and the DRL-enhanced MCTS outperforms both DRL and vanilla MCTS in the hardest levels. We conclude that player modelling via automated playtesting can benefit from combining DRL and MCTS. Moreover, it can be worthwhile to investigate a subset of repeated best AI agent runs, if AI gameplay does not yield good predictions on average.