Investigations into Playing Chess Endgames using Reinforcement Learning.

Research in computer game playing has relied primarily on brute force searching approaches rather than any formal AI method. However, these methods may not be able to exceed human ability, as they need human expert knowledge to perform as well as they do. One recently popularized field of research known as reinforcement learning has shown good prospects in overcoming these limitations when applied to non-deterministic games. This thesis investigated whether the TD(_) algorithm, one method of reinforcement learning, using standard back-propagation neural networks for function generalization, could successfully learn a deterministic game such as chess. The aim is to determine if an agent using no external knowledge can learn to defeat a random player consistently. The results of this thesis suggests that, even though the agents faced a highly information sparse environment, an agent using a well selected view of the state information was still able to learn to not only to differentiate between various terminating board positions but also to improve its play against a random player. This shows that the reinforcement learning techniques are quite capable of learning behaviour in large deterministic environments without needing any external knowledge.