A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery
This addresses the challenge for game developers in making informed balance decisions to avoid unforeseen consequences in competitive games.
The paper tackles the problem of predicting the impact of game balance changes on metagames, using a Reinforcement Learning-based framework to achieve high accuracy in predicting outcomes for competitive Pokémon tiers.
A metagame is a collection of knowledge that goes beyond the rules of a game. In competitive, team-based games like Pokémon 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 Pokémon Showdown, a collection of competitive Pokémon tiers, with high accuracy.