# A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery

arXiv: https://arxiv.org/abs/2409.07340 · HTML: https://mlsanity.com/paper/2409.07340/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess](https://mlsanity.com/paper/2009.04374/) (2020)
- [Automatic Playtesting for Game Parameter Tuning via Active Learning](https://mlsanity.com/paper/1908.01417/) (2019)
- [Evolving the Hearthstone Meta](https://mlsanity.com/paper/1907.01623/) (2019)
- [Metagame Autobalancing for Competitive Multiplayer Games](https://mlsanity.com/paper/2006.04419/) (2020)

## Abstract (arXiv)

A metagame is a collection of knowledge that goes beyond the rules of a game. In competitive, team-based games like Pok'emon or League of Legends, it refers to the set of current dominant characters and/or strategies within the player base. Developer changes to the balance of the game can have drastic and unforeseen consequences on these sets of meta characters. A framework for predicting the impact of balance changes could aid developers in making more informed balance decisions. In this paper we present such a Meta Discovery framework, leveraging Reinforcement Learning for automated testing of balance changes. Our results demonstrate the ability to predict the outcome of balance changes in Pok'emon Showdown, a collection of competitive Pok'emon tiers, with high accuracy.