Learning to play Tic-tac-toe

This paper reports our experiment on applying Q Learning algorithm for learning to play Tic-tac-toe. The original algorithm is modified by updating the Q value only when the game terminates, propagating the update process from the final move backward to the first move, and incorporating a new update rule. We evaluate the agent performance using full-board and partial-board representations. In this evaluation, the agent plays the tic-tac-toe game against human players. The evaluation results show that the performance of modified Q Learning algorithm with partial-board representation is comparable to that of human players.