# Augmenting Automated Game Testing with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2103.15819 · HTML: https://mlsanity.com/paper/2103.15819/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [Playing FPS Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05521/) (2016)
- [Imitation Learning with Concurrent Actions in 3D Games](https://mlsanity.com/paper/1803.05402/) (2018)
- [Automatic Playtesting for Game Parameter Tuning via Active Learning](https://mlsanity.com/paper/1908.01417/) (2019)

## Built on by (most cited first, 30 of 38)

- [Direct Behavior Specification via Constrained Reinforcement Learning](https://mlsanity.com/paper/2112.12228/) (2021)
- [CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning](https://mlsanity.com/paper/2203.11096/) (2022)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning](https://mlsanity.com/paper/2208.07811/) (2022)
- [Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games](https://mlsanity.com/paper/2307.11105/) (2023)
- [Automated Play-Testing Through RL Based Human-Like Play-Styles Generation](https://mlsanity.com/paper/2211.17188/) (2022)
- [Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation](https://mlsanity.com/paper/2207.08379/) (2022)
- [Learning to Identify Perceptual Bugs in 3D Video Games](https://mlsanity.com/paper/2202.12884/) (2022)
- [Predicting Game Engagement and Difficulty Using AI Players](https://mlsanity.com/paper/2107.12061/) (2021)
- [Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Games](https://mlsanity.com/paper/2406.08231/) (2024)
- [Improving Generalization in Game Agents with Data Augmentation in Imitation Learning](https://mlsanity.com/paper/2309.12815/) (2023)
- [Distilling Reinforcement Learning Tricks for Video Games](https://mlsanity.com/paper/2107.00703/) (2021)
- [World of Bugs: A Platform for Automated Bug Detection in 3D Video Games](https://mlsanity.com/paper/2206.11037/) (2022)
- [A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks](https://mlsanity.com/paper/2208.12136/) (2022)
- [Go-Explore Complex 3D Game Environments for Automated Reachability Testing](https://mlsanity.com/paper/2209.00570/) (2022)
- [Generating Personas for Games with Multimodal Adversarial Imitation Learning](https://mlsanity.com/paper/2308.07598/) (2023)
- [Preference-conditioned Pixel-based AI Agent For Game Testing](https://mlsanity.com/paper/2308.09289/) (2023)
- [Return-Aligned Decision Transformer](https://mlsanity.com/paper/2402.03923/) (2024)
- [Reinforcement Learning for High-Level Strategic Control in Tower Defense Games](https://mlsanity.com/paper/2406.07980/) (2024)
- [Bayesian Optimization-based Search for Agent Control in Automated Game Testing](https://mlsanity.com/paper/2508.13121/) (2025)
- [Open-Ended Video Game Glitch Detection with Agentic Reasoning and Temporal Grounding](https://mlsanity.com/paper/2604.07818/) (2026)
- [Automatic Testing and Validation of Level of Detail Reductions Through Supervised Learning](https://mlsanity.com/paper/2208.12674/) (2022)
- [PlotMap: Automated Layout Design for Building Game Worlds](https://mlsanity.com/paper/2309.15242/) (2023)
- [Synergizing Code Coverage and Gameplay Intent: Coverage-Aware Game Playtesting with LLM-Guided Reinforcement Learning](https://mlsanity.com/paper/2512.12706/) (2025)
- [Task Relabelling for Multi-task Transfer using Successor Features](https://mlsanity.com/paper/2205.10175/) (2022)
- [Automatic Bug Detection in Games using LSTM Networks](https://mlsanity.com/paper/2312.08418/) (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)
- [Aligning Agents like Large Language Models](https://mlsanity.com/paper/2406.04208/) (2024)
- [A Benchmark Environment for Offline Reinforcement Learning in Racing Games](https://mlsanity.com/paper/2407.09415/) (2024)
- [Leveraging Reward Models for Guiding Code Review Comment Generation](https://mlsanity.com/paper/2506.04464/) (2025)

## Abstract (arXiv)

General game testing relies on the use of human play testers, play test scripting, and prior knowledge of areas of interest to produce relevant test data. Using deep reinforcement learning (DRL), we introduce a self-learning mechanism to the game testing framework. With DRL, the framework is capable of exploring and/or exploiting the game mechanics based on a user-defined, reinforcing reward signal. As a result, test coverage is increased and unintended game play mechanics, exploits and bugs are discovered in a multitude of game types. In this paper, we show that DRL can be used to increase test coverage, find exploits, test map difficulty, and to detect common problems that arise in the testing of first-person shooter (FPS) games.