Puzzle Progression Game Using Reinforcement Learning

This paper presents an innovative approach to puzzle progression games by integrating reinforcement learning (RL), specifically Q-learning, to dynamically adjust the puzzle difficulty and enhance the player experience. Unlike traditional puzzle games, which employ manually designed levels with fixed difficulty curves, this system utilizes RL to create an evolving challenge tailored to the player's skill level. The RL agent, trained through trial and error, progressively learns optimal strategies for solving increasingly complex puzzles without requiring a predefined model of the environment. Experimental results demonstrate that RL-based agents outperform traditional rule-based systems in terms of adaptability, efficiency, and scalability. The paper also explores potential future developments, including hybrid models that integrate deep learning with RL to tackle more complex puzzles and the possibility of multi-agent environments. The work highlights the benefits of RL in revolutionizing game design, providing dynamic, player-responsive experiences.