# Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation

arXiv: https://arxiv.org/abs/2207.08379 · HTML: https://mlsanity.com/paper/2207.08379/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Unifying Count-Based Exploration and Intrinsic Motivation](https://mlsanity.com/paper/1606.01868/) (2016)
- [Frustratingly Simple Few-Shot Object Detection](https://mlsanity.com/paper/2003.06957/) (2020)
- [A survey on intrinsic motivation in reinforcement learning](https://mlsanity.com/paper/1908.06976/) (2019)
- [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)
- [Adversarial Reinforcement Learning for Procedural Content Generation](https://mlsanity.com/paper/2103.04847/) (2021)
- [Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics](https://mlsanity.com/paper/1802.06881/) (2018)
- [Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents](https://mlsanity.com/paper/2103.13798/) (2021)
- [Deep Reinforcement Learning for General Video Game AI](https://mlsanity.com/paper/1806.02448/) (2018)
- [Automated Video Game Testing Using Synthetic and Human-Like Agents](https://mlsanity.com/paper/1906.00317/) (2019)
- [You May Not Need Ratio Clipping in PPO](https://mlsanity.com/paper/2202.00079/) (2022)
- [A Survey of Video Game Testing](https://mlsanity.com/paper/2103.06431/) (2021)
- [Winning Isn't Everything: Enhancing Game Development with Intelligent Agents](https://mlsanity.com/paper/1903.10545/) (2019)
- [Automated Playtesting of Matching Tile Games](https://mlsanity.com/paper/1907.06570/) (2019)
- [Taking the Scenic Route: Automatic Exploration for Videogames](https://mlsanity.com/paper/1812.03125/) (2018)
- [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)
- [Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing](https://mlsanity.com/paper/2003.07813/) (2020)
- [Critical Success Factors to Improve the Game Development Process from a Developers Perspective](https://mlsanity.com/paper/1801.04293/) (2018)

## Built on by (most cited first, 7 of 7)

- [Go-Explore Complex 3D Game Environments for Automated Reachability Testing](https://mlsanity.com/paper/2209.00570/) (2022)
- [Preference-conditioned Pixel-based AI Agent For Game Testing](https://mlsanity.com/paper/2308.09289/) (2023)
- [Open-Ended Video Game Glitch Detection with Agentic Reasoning and Temporal Grounding](https://mlsanity.com/paper/2604.07818/) (2026)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Human-AI Collaborative Game Testing with Vision Language Models](https://mlsanity.com/paper/2501.11782/) (2025)
- [A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents](https://mlsanity.com/paper/2505.16801/) (2025)
- [CA2: Code-Aware Agent for Automated Game Testing](https://mlsanity.com/paper/2605.13918/) (2026)

## Abstract (arXiv)

Deep reinforcement learning (DRL) has attracted much attention in automated game testing. Early attempts rely on game internal information for game space exploration, thus requiring deep integration with games, which is inconvenient for practical applications. In this work, we propose using only screenshots/pixels as input for automated game testing and build a general game testing agent, Inspector, that can be easily applied to different games without deep integration with games. In addition to covering all game space for testing, our agent tries to take human-like behaviors to interact with key objects in a game, since some bugs usually happen in player-object interactions. Inspector is based on purely pixel inputs and comprises three key modules: game space explorer, key object detector, and human-like object investigator. Game space explorer aims to explore the whole game space by using a curiosity-based reward function with pixel inputs. Key object detector aims to detect key objects in a game, based on a small number of labeled screenshots. Human-like object investigator aims to mimic human behaviors for investigating key objects via imitation learning. We conduct experiments on two popular video games: Shooter Game and Action RPG Game. Experiment results demonstrate the effectiveness of Inspector in exploring game space, detecting key objects, and investigating objects. Moreover, Inspector successfully discovers two potential bugs in those two games. The demo video of Inspector is available at https://github.com/Inspector-GameTesting/Inspector-GameTesting.