Harangi, BalázsKaddah, Zohair2025-06-262025-06-262025-04-16https://hdl.handle.net/2437/394786My thesis explores the use of reinforcement learning to develop intelligent agents in a basic 3D tank battle environment. The project's main goal is to train an AI agent to move around the game space, aim, and attack using the Proximal Policy Optimization algorithm. I used Unity to create the project and set up the ML-Agents Toolkit for training. The AI agent demonstrated basic tactical behaviors such as seeking enemies and avoiding obstacles. Overall, the thesis highlights the importance of reinforcement learning in game development.42enArtificial IntelligenceGame DevelopmentReinforcement LearningInformatics::Computer ScienceHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.