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.
@article{arxiv.2409.07340,
title = {A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery},
author = {Akash Saravanan and Matthew Guzdial},
journal= {arXiv preprint arXiv:2409.07340},
year = {2024}
}