Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN
This addresses the problem of inefficient scaling in board game AI for researchers and developers, offering a novel incremental learning approach.
The paper tackles the challenge of scaling board game AI by introducing ScalableAlphaZero, which learns incrementally on small boards and transfers to large ones, achieving a win over AlphaZero on a large Othello board after only three days of training compared to 30 days.
Playing board games is considered a major challenge for both humans and AI researchers. Because some complicated board games are quite hard to learn, humans usually begin with playing on smaller boards and incrementally advance to master larger board strategies. Most neural network frameworks that are currently tasked with playing board games neither perform such incremental learning nor possess capabilities to automatically scale up. In this work, we look at the board as a graph and combine a graph neural network architecture inside the AlphaZero framework, along with some other innovative improvements. Our ScalableAlphaZero is capable of learning to play incrementally on small boards, and advancing to play on large ones. Our model can be trained quickly to play different challenging board games on multiple board sizes, without using any domain knowledge. We demonstrate the effectiveness of ScalableAlphaZero and show, for example, that by training it for only three days on small Othello boards, it can defeat the AlphaZero model on a large board, which was trained to play the large board for $30$ days.