Mastering Water Sort Puzzle: A Reinforcement Learning Approach with Deep Q-Learning Techniques

Water Sort Puzzle, similar to Color Sort Puzzle, offers an engaging and addictive gaming experience where players aim to match colors in each vial. While easy to grasp, mastering the game requires strategic thinking, often necessitating players to backtrack and iterate through multiple attempts, especially at higher levels. In this paper, we delve into the application of Reinforcement Learning (RL) to conquer the Water Sort Puzzle. Our approach includes investigating Deep Qlearning (DQN) and Double Deep Qlearning (DDQN) techniques. The trained models found optimal solutions for configurations with a smaller number of vials, such as those with 5 vials. However, for more complex setups, such as those with 14 vials, although our approach outperformed random policies, further training is necessary to achieve optimal performance in terms of minimizing the number of actions required to solve the game. For a number of vials of 14, it performed better than the random policy but needs more training to win the game with an optimal number of actions.