The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are a remarkable demonstration of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these promising approaches by the research community. Toward elucidating unresolved mysteries and facilitating future research, we propose ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals. We apply ELF OpenGo to conduct extensive ablation studies, and to identify and analyze numerous interesting phenomena in both the model training and in the gameplay inference procedures. Our code, models, selfplay datasets, and auxiliary data are publicly available.

本研究提出 ELF OpenGo，这是第一个开源的围棋 AI，其重新实现了 AlphaZero 算法并以完美的战绩（20：0）与国际顶级专业人士相比表明了超人类性能。我们对 ELF OpenGo 进行了广泛的消融研究，并在模型训练和游戏推理过程中分析出许多有趣的现象。

ELF OpenGo:AlphaZero模型的开放性实现和分析