We introduce OLIVAW, an AI Othello player adopting the design principles of the famous AlphaGo series. The main motivation behind OLIVAW was to attain exceptional competence in a non-trivial board game at a tiny fraction of the cost of its illustrious predecessors. In this paper, we show how the AlphaGo Zero's paradigm can be successfully applied to the popular game of Othello using only commodity hardware and free cloud services. While being simpler than Chess or Go, Othello maintains a considerable search space and difficulty in evaluating board positions. To achieve this result, OLIVAW implements 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 of OLIVAW in three different ways: by pitting it against Edax, the strongest open-source Othello engine, 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.

翻译：我们引入了AI Othello球员的OLIVAW, 采用著名的AlphaGo系列的设计原则。OLIVAW的主要动机是,以其杰出前辈的一小部分成本,在非三重棋盘游戏中取得特殊能力。在本文中,我们展示了阿尔法戈零点模型如何仅使用商品硬件和免费云服务成功地应用于奥瑟洛流行游戏。Othello虽然比Ches或Go更简单,但在评价董事会位置方面却拥有相当大的搜索空间和困难。为了实现这一结果,OLIVAW实施了一些由最近工作启发的改进,以加速标准阿尔法戈Zero学习进程。主要修改意味着在培训阶段将每场游戏收集的职位翻一番,包括代理人没有发挥但基本上探索的职位。我们用三种不同的方式测试了ALGOVAW的力量:在网络平台OthelloQest上玩匿名游戏,以及最后两次人际比赛,即国家冠军和前世界冠军。