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

TL;DR

This paper introduces a Meta Discovery framework that uses Reinforcement Learning to predict the effects of game balance changes on the meta in competitive games like Pokémon, aiding developers in decision-making.

Contribution

The paper presents a novel Reinforcement Learning-based framework for predicting the impact of game balance changes on the meta, improving decision support for game developers.

Findings

High accuracy in predicting balance change outcomes in Pokémon Showdown

Effective automated testing of game balance modifications

Framework applicable to competitive, team-based games

Abstract

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.