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

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.