# Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding

arXiv: https://arxiv.org/abs/2112.03534 · HTML: https://mlsanity.com/paper/2112.03534/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2021.

## Built on by (most cited first, 26 of 26)

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- [Surrogate Assisted Generation of Human-Robot Interaction Scenarios](https://mlsanity.com/paper/2304.13787/) (2023)
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- [Efficient Quality-Diversity Optimization through Diverse Quality Species](https://mlsanity.com/paper/2304.07425/) (2023)
- [Generative Design through Quality-Diversity Data Synthesis and Language Models](https://mlsanity.com/paper/2405.09997/) (2024)
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- [Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2602.23468/) (2026)
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- [Fitness Approximation through Machine Learning](https://mlsanity.com/paper/2309.03318/) (2023)
- [Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables](https://mlsanity.com/paper/2310.05955/) (2023)
- [Evolutionary Machine Learning and Games](https://mlsanity.com/paper/2311.16172/) (2023)
- [Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report](https://mlsanity.com/paper/2412.14085/) (2024)
- [Quality-Diversity Optimization as Multi-Objective Optimization](https://mlsanity.com/paper/2602.00478/) (2026)
- [U-Net-Accelerated Quality-Diversity Optimization for Climate-Adaptive Urban Layouts](https://mlsanity.com/paper/2606.04658/) (2026)

## Abstract (arXiv)

We study the problem of efficiently generating high-quality and diverse content in games. Previous work on automated deckbuilding in Hearthstone shows that the quality diversity algorithm MAP-Elites can generate a collection of high-performing decks with diverse strategic gameplay. However, MAP-Elites requires a large number of expensive evaluations to discover a diverse collection of decks. We propose assisting MAP-Elites with a deep surrogate model trained online to predict game outcomes with respect to candidate decks. MAP-Elites discovers a diverse dataset to improve the surrogate model accuracy, while the surrogate model helps guide MAP-Elites towards promising new content. In a Hearthstone deckbuilding case study, we show that our approach improves the sample efficiency of MAP-Elites and outperforms a model trained offline with random decks, as well as a linear surrogate model baseline, setting a new state-of-the-art for quality diversity approaches in automated Hearthstone deckbuilding. We include the source code for all the experiments at: https://github.com/icaros-usc/EvoStone2.