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 subset security-related issues. In this paper, we implement and test an system whose main goal to help differentiate human players from AI For purpose, first developed multiplayer game inside engine Unity would serve framework for training reinforcement learning agent. This agent thus learn bots within game. We implemented Machine Learning Agents Toolkit library, uses proximal policy optimization algorithm. state machines, perform certain actions depending on condition satisfied. Two experiments were carried out testing showed promising results identifying artificial