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, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pokémon</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">League of Legends</i>, 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 article, 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pokémon Showdown</i>, a collection of competitive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pokémon</i> tiers, with high accuracy.