Implementation of AlphaZero focused on learning to play the Dots and Boxes game (although chess is included as a swappable component). Following the AlphaZero method, through self-play, the agent learns a policy and value network that guides a Monte Carlo tree search on game states. On a 5x5 board, it learns to outplay an agent that uses Monte Carlo tree search with uninitialized policy and value network.