# Evolving the Hearthstone Meta

arXiv: https://arxiv.org/abs/1907.01623 · HTML: https://mlsanity.com/paper/1907.01623/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [Summarizing Strategy Card Game AI Competition](https://mlsanity.com/paper/2305.11814/) (2023)
- [Evolving Evaluation Functions for Collectible Card Game AI](https://mlsanity.com/paper/2105.01115/) (2021)
- [Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games](https://mlsanity.com/paper/2209.10055/) (2022)
- [Introducing Tales of Tribute AI Competition](https://mlsanity.com/paper/2305.08234/) (2023)
- [Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis](https://mlsanity.com/paper/2408.17180/) (2024)
- [A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery](https://mlsanity.com/paper/2409.07340/) (2024)
- [Simulation-Driven Balancing of Competitive Game Levels with Reinforcement Learning](https://mlsanity.com/paper/2503.18748/) (2025)
- [Level the Level: Balancing Game Levels for Asymmetric Player Archetypes With Reinforcement Learning](https://mlsanity.com/paper/2503.24099/) (2025)

## Abstract (arXiv)

Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system. Tuning over 2000 cards to generate the desired outcome without disrupting the existing environment becomes a laborious challenge. In this paper, we discuss the impacts that changes to existing cards can have on strategy in Hearthstone. By analyzing the win rate on match-ups across different decks, being played by different strategies, we propose to compare their performance before and after changes are made to improve or worsen different cards. Then, using an evolutionary algorithm, we search for a combination of changes to the card attributes that cause the decks to approach equal, 50% win rates. We then expand our evolutionary algorithm to a multi-objective solution to search for this result, while making the minimum amount of changes, and as a consequence disruption, to the existing cards. Lastly, we propose and evaluate metrics to serve as heuristics with which to decide which cards to target with balance changes.