Anticheat System Based on Reinforcement Learning Agents in Unity (CROSBI ID 307816)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Lukaš, Mihael ; Tomičić, Igor ; Bernik, Andrija
engleski
Anticheat System Based on Reinforcement Learning Agents in Unity
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.
security ; artificial intelligence ; infosec ; reinforcement learning ; agents ; games ; gaming ; unity
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
Trošak objave rada u otvorenom pristupu
Povezanost rada
Informacijske i komunikacijske znanosti, Računarstvo