# Automated Video Game Testing Using Synthetic and Human-Like Agents

arXiv: https://arxiv.org/abs/1906.00317 · HTML: https://mlsanity.com/paper/1906.00317/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

## Built on by (most cited first, 22 of 22)

- [GameGen-Verifier: Parallel Keypoint-Based Verification for LLM-Generated Games via Runtime State Injection](https://mlsanity.com/paper/2605.07442/) (2026)
- [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)
- [Robust Imitation Learning for Automated Game Testing](https://mlsanity.com/paper/2401.04572/) (2024)
- [Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL](https://mlsanity.com/paper/2102.11893/) (2021)
- [World of Bugs: A Platform for Automated Bug Detection in 3D Video Games](https://mlsanity.com/paper/2206.11037/) (2022)
- [Go-Explore Complex 3D Game Environments for Automated Reachability Testing](https://mlsanity.com/paper/2209.00570/) (2022)
- [Return-Aligned Decision Transformer](https://mlsanity.com/paper/2402.03923/) (2024)
- [Level Up Your Tutorials: VLMs for Game Tutorials Quality Assessment](https://mlsanity.com/paper/2408.08396/) (2024)
- [Open-Ended Video Game Glitch Detection with Agentic Reasoning and Temporal Grounding](https://mlsanity.com/paper/2604.07818/) (2026)
- [Playtesting: What is Beyond Personas](https://mlsanity.com/paper/2107.11965/) (2021)
- [Synergizing Code Coverage and Gameplay Intent: Coverage-Aware Game Playtesting with LLM-Guided Reinforcement Learning](https://mlsanity.com/paper/2512.12706/) (2025)
- [The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study](https://mlsanity.com/paper/2206.10210/) (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)
- [On Automating Video Game Regression Testing by Planning and Learning](https://mlsanity.com/paper/2402.12393/) (2024)
- [Human-AI Collaborative Game Testing with Vision Language Models](https://mlsanity.com/paper/2501.11782/) (2025)
- [Leveraging Reward Models for Guiding Code Review Comment Generation](https://mlsanity.com/paper/2506.04464/) (2025)
- [Knowledge Graph-enhanced Large Language Model for Incremental Game PlayTesting](https://mlsanity.com/paper/2511.02534/) (2025)
- [Cooperative Multi-agent Approach for Automated Computer Game Testing](https://mlsanity.com/paper/2405.11347/) (2024)
- [Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26](https://mlsanity.com/paper/2607.07498/) (2026)

## Abstract (arXiv)

In this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like- and two distinct approaches to create them. Our agents are derived from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but focus on finding defects. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The human-like agent uses test goals extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm from tester trajectories. MGPIRL captures multiple policies executed by human testers. These testers' aims are finding defects while interacting with the game to break it, which is considerably different from game playing. We present interaction states to model such interactions. We use our agents to produce test sequences, run the game with these sequences, and check the game for each run with an automated test oracle. We analyze the proposed method in two parts: we compare the success of human-like and synthetic agents in bug finding, and we evaluate the similarity between humanlike agents and human testers. We collected 427 trajectories from human testers using the General Video Game Artificial Intelligence (GVG-AI) framework and created three games with 12 levels that contain 45 bugs. Our experiments reveal that human-like and synthetic agents compete with human testers' bug finding performances. Moreover, we show that MGP-IRL increases the human-likeness of agents while improving the bug finding performance.