Application of Reinforcement Learning to Solve Rubrik’s Cube with Markov Decision Process

The Rubik's Cube is a complex puzzle with an enormous number of possible configurations, making it a challenging problem for both humans and computational methods to solve. While traditional solving algorithms rely on predefined strategies, this study explores the application of reinforcement learning (RL) to develop an adaptive and efficient solution model. This study aims to create an RL_based solver using the Markov Decision Process (MDP) framework, optimizing for speed, move efficiency, and solving steps. The proposed model employs Q-learning and Monte Carlo Tree Search (MCTS) to determine the optimal actions at each game state, which are trained through extensive Rubik's Cube simulations. The key novelty of this study lies in the integration of MCTS with Q-learning to enhance decision-making efficiency by reducing the number of moves compared with conventional methods. The experimental results demonstrate that the model achieves near-optimal solutions with fewer moves, outperforming basic rule-based approaches. Additionally, a web-based application was developed to provide real-time solving strategies based on user-input cube configurations. This study contributes to the advancement of RL applications in combinatorial puzzles and offers a practical tool for Rubik's Cube enthusiasts seeking to improve their solving techniques.