# Robust Imitation Learning for Automated Game Testing

arXiv: https://arxiv.org/abs/2401.04572 · HTML: https://mlsanity.com/paper/2401.04572/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 18 of 18)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One](https://mlsanity.com/paper/1912.03263/) (2019)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations](https://mlsanity.com/paper/2107.14483/) (2021)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [Energy-based Generative Adversarial Network](https://mlsanity.com/paper/1609.03126/) (2016)
- [Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration](https://mlsanity.com/paper/2105.06411/) (2021)
- [Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation](https://mlsanity.com/paper/1905.06750/) (2019)
- [Discrete and Continuous Action Representation for Practical RL in Video Games](https://mlsanity.com/paper/1912.11077/) (2019)
- [How to Train Your Energy-Based Model for Regression](https://mlsanity.com/paper/2005.01698/) (2020)
- [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)
- [Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specifications](https://mlsanity.com/paper/2201.12231/) (2022)

## Built on by (most cited first, 4 of 4)

- [Learning Representations in Video Game Agents with Supervised Contrastive Imitation Learning](https://mlsanity.com/paper/2509.11880/) (2025)
- [MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models](https://mlsanity.com/paper/2604.07752/) (2026)
- [CA2: Code-Aware Agent for Automated Game Testing](https://mlsanity.com/paper/2605.13918/) (2026)
- [Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA](https://mlsanity.com/paper/2607.25921/) (2026)

## Abstract (arXiv)

Game development is a long process that involves many stages before a product is ready for the market. Human play testing is among the most time consuming, as testers are required to repeatedly perform tasks in the search for errors in the code. Therefore, automated testing is seen as a key technology for the gaming industry, as it would dramatically improve development costs and efficiency. Toward this end, we propose EVOLUTE, a novel imitation learning-based architecture that combines behavioural cloning (BC) with energy based models (EBMs). EVOLUTE is a two-stream ensemble model that splits the action space of autonomous agents into continuous and discrete tasks. The EBM stream handles the continuous tasks, to have a more refined and adaptive control, while the BC stream handles discrete actions, to ease training. We evaluate the performance of EVOLUTE in a shooting-and-driving game, where the agent is required to navigate and continuously identify targets to attack. The proposed model has higher generalisation capabilities than standard BC approaches, showing a wider range of behaviours and higher performances. Also, EVOLUTE is easier to train than a pure end-to-end EBM model, as discrete tasks can be quite sparse in the dataset and cause model training to explore a much wider set of possible actions while training.