# Learning to Identify Perceptual Bugs in 3D Video Games

arXiv: https://arxiv.org/abs/2202.12884 · HTML: https://mlsanity.com/paper/2202.12884/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [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)
- [Automated Video Game Testing Using Synthetic and Human-Like Agents](https://mlsanity.com/paper/1906.00317/) (2019)
- [A Survey of Video Game Testing](https://mlsanity.com/paper/2103.06431/) (2021)
- [Visual Anomaly Detection for Images: A Survey](https://mlsanity.com/paper/2109.13157/) (2021)
- [Computer Games Are Serious Business and so is their Quality: Particularities of Software Testing in Game Development from the Perspective of Practitioners](https://mlsanity.com/paper/1812.05164/) (2018)
- [Navigation and Exploration in 3D-Game Automated Play Testing](https://mlsanity.com/paper/2009.07015/) (2020)

## Built on by (most cited first, 6 of 6)

- [GlitchBench: Can large multimodal models detect video game glitches?](https://mlsanity.com/paper/2312.05291/) (2023)
- [PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos](https://mlsanity.com/paper/2412.01800/) (2024)
- [VideoGameQA-Bench: Evaluating Vision-Language Models for Video Game Quality Assurance](https://mlsanity.com/paper/2505.15952/) (2025)
- [Open-Ended Video Game Glitch Detection with Agentic Reasoning and Temporal Grounding](https://mlsanity.com/paper/2604.07818/) (2026)
- [Automatic Bug Detection in Games using LSTM Networks](https://mlsanity.com/paper/2312.08418/) (2023)
- [Multi-Label Perceptual Bug Detection in Video Games using Deep Learning on Gameplay Footage](https://mlsanity.com/paper/2610.08593/) (2026)

## Abstract (arXiv)

Automated Bug Detection (ABD) in video games is composed of two distinct but complementary problems: automated game exploration and bug identification. Automated game exploration has received much recent attention, spurred on by developments in fields such as reinforcement learning. The complementary problem of identifying the bugs present in a player's experience has for the most part relied on the manual specification of rules. Although it is widely recognised that many bugs of interest cannot be identified with such methods, little progress has been made in this direction. In this work we show that it is possible to identify a range of perceptual bugs using learning-based methods by making use of only the rendered game screen as seen by the player. To support our work, we have developed World of Bugs (WOB) an open platform for testing ABD methods in 3D game environments.