OLIVAW: Mastering Othello without Human Knowledge, nor a Fortune
This work provides a low-cost solution for mastering Othello, making advanced AI accessible without expensive infrastructure, though it is incremental as it builds on existing AlphaGo Zero methods.
The authors tackled the problem of achieving high-level Othello play with minimal resources by adapting AlphaGo Zero's paradigm to Othello using commodity hardware and free cloud services, resulting in OLIVAW, which demonstrated strong performance by defeating the top open-source engine Edax and competing against human champions.
We introduce OLIVAW, an AI Othello player adopting the design principles of the famous AlphaGo programs. 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.