文章基本信息

标题：Anticheat System Based on Reinforcement Learning Agents in Unity
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作者：Mihael Lukas ; Igor Tomicic ; Andrija Bernik 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2022
卷号：13
期号：4
页码：173
DOI：10.3390/info13040173
语种：English
出版社：MDPI Publishing
摘要：Game cheating is a common occurrence that may degrade the experience of “honest” players. It can be hindered by using appropriate anticheat systems, which are being considered as a subset of security-related issues. In this paper, we implement and test an anticheat system whose main goal is to help differentiate human players from AI players. For this purpose, we first developed a multiplayer game inside game engine Unity that would serve as a framework for training the reinforcement learning agent. This agent would thus learn to differentiate human players from bots within the game. We implemented the Machine Learning Agents Toolkit library, which uses the proximal policy optimization algorithm. AI players are implemented using state machines, and perform certain actions depending on which condition is satisfied. Two experiments were carried out for testing the agent and showed promising results for identifying artificial players.