Reinforcement Learning (RL) has emerged as a powerful machine learning technique for developing intelligent systems capable of learning optimal strategies in complex environments. Algorithms implementing RL have shown remarkable success in mastering gameplay for various board games, competing against both established game-playing algorithms and experienced human players. This project aims to develop an RL agent  capable of mastering the solo print and play game Utopia Engine Beast Hunter (UEBH).