Olivaw: Mastering Othello Without Human Knowledge, nor a Fortune
Antonio Norelli, Alessandro Panconesi · IEEE Transactions on Games · 2022
In this article, we introduceOlivaw, an artificial intelligenceOthelloplayer adopting the design principles of the famous AlphaGo programs. The main motivation behindOlivawis to attain exceptional competence in a nontrivial board game at a tiny fraction of the cost of its illustrious predecessors. In this article, we show how the AlphaGo Zero’s paradigm can be successfully applied to the popular game ofOthellousing only commodity hardware and free cloud services. While being simpler thanChessorGo,Othellomaintains a considerable search space and difficulty in evaluating board positions. To achieve this result,Olivawimplements some improvements inspired by recent works to accelerate the standard AlphaGo Zero learning process. The main modification implies doubling the positions collected per game during the training phase, by including also positions not played but largely explored by the agent. We tested the strength ofOlivawin three different ways: by pitting it against Edax, considered by the strongest open-sourceOthelloengine, by playing anonymous games on the web platform OthelloQuest, and, finally, in two in-person matches against top-notch human players: a national champion and a former world champion.