Artificial Intelligence for the Santorini Board Game

The aim of this thesis was to use create an intelligent agent using Reinforcement learning to play Santorini, a 2-player zero-sum board game. The specific algorithm that was implemented was a modified version of Deep Q-learning, with the use of convolutional neural networks (one for training and the other for estimating future Q-value) and a memory of previously executed moves, from which the agent chooses randomly during training. Numerous experiments resulted in 2 final models. One was trained by playing against basic bots, with gradually increasing difficulty. The other was trained by playing against itself from the start. The outcome shows that the model playing against itself produces better results, however both models still perform worse than a bot which uses heuristic function