# Bayesian Optimization-based Search for Agent Control in Automated Game Testing

arXiv: https://arxiv.org/abs/2508.13121 · HTML: https://mlsanity.com/paper/2508.13121/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [A Survey of Deep Reinforcement Learning in Video Games](https://mlsanity.com/paper/1912.10944/) (2019)
- [Augmenting Automated Game Testing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.15819/) (2021)
- [Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents](https://mlsanity.com/paper/2103.13798/) (2021)
- [Winning Isn't Everything: Enhancing Game Development with Intelligent Agents](https://mlsanity.com/paper/1903.10545/) (2019)
- [Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning](https://mlsanity.com/paper/2208.07811/) (2022)
- [Using Reinforcement Learning for Load Testing of Video Games](https://mlsanity.com/paper/2201.06865/) (2022)

## Built on by (most cited first, 3 of 3)

- [Learning Representations in Video Game Agents with Supervised Contrastive Imitation Learning](https://mlsanity.com/paper/2509.11880/) (2025)
- [RuleSmith: Multi-Agent LLMs for Automated Game Balancing](https://mlsanity.com/paper/2602.06232/) (2026)
- [Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA](https://mlsanity.com/paper/2607.25921/) (2026)

## Abstract (arXiv)

This work introduces an automated testing approach that employs agents controlling game characters to detect potential bugs within a game level. Harnessing the power of Bayesian Optimization (BO) to execute sample-efficient search, the method determines the next sampling point by analyzing the data collected so far and calculates the data point that will maximize information acquisition. To support the BO process, we introduce a game testing-specific model built on top of a grid map, that features the smoothness and uncertainty estimation required by BO, however and most importantly, it does not suffer the scalability issues that traditional models carry. The experiments demonstrate that the approach significantly improves map coverage capabilities in both time efficiency and exploration distribution.