用强化学习预测游戏平衡调整对主流角色的影响
A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery
- 通过强化学习自动测试平衡调整方案
- 在宝可梦对战平台预测准确率达高
- 适合游戏开发者和平衡设计者参考
元游戏是超越游戏规则的玩家知识集合，在《宝可梦》或《英雄联盟》这类团队竞技游戏中，指当前玩家群体中占主导地位的角色与策略组合。开发者对游戏平衡的调整可能对这些元角色产生剧烈且不可预见的影响。本文提出一种元发现框架，利用强化学习对平衡变化进行自动化测试。实验结果表明，该框架能够以高精度预测《宝可梦对战》（Pokémon Showdown）中多个竞争性梯队的平衡调整后果。
原文摘要 · Abstract (English)
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.
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