Virtual Reality based 3D Mario-style Game Using Procedural Content Generation and Reinforcement Learning

The rapid growth of machine learning has created new potential for improving the gaming experience, particularly in virtual reality (VR) situations. This study addresses the difficulty of developing dynamic, engaging virtual reality games by integrating procedural content generation (PCG) and reinforcement learning (RL). The motivation originates from the desire to create adaptive gaming systems that respond to individual player behaviour, improving immersion and variability in games. The major goal is to look into how PCG can be used to automate the production of game elements, as well as how RL, notably Q-learning, may be used to alter game mechanics in real time in response to player interactions. The primary contribution of this work lies in integrating these advanced techniques inside a VR framework and evaluating their influence on the gamer.