Abstract. The game of Hex can be played on multiple boardsizes. Transferring neural net knowledge learned on one boardsize to other boardsizes is of interest, since deep neural nets usually require large size of high quality data to train, whereas expert games can be unavailable or difficult to generate. In this paper we investigate neural transfer learning in Hex. We show that when only boardsize independent neurons are used, the resulting neural net obtained from training on one base boardsize can effectively generalize — without fine-tuning — to multiple target boardsizes, larger or smaller. When transferring to larger boardsizes, fine-tuning provides faster learning and better performance. The strength of the transferable network can be amplified with search: with a single neural net model trained on games from a base boardsize, we obtain players stronger than MoHex 2.0 on multiple target boardsizes.

中文翻译：

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Hex 的可转移神经网络

摘要。Hex 游戏可以在多种棋盘尺寸上进行。将在一种棋盘上学到的神经网络知识转移到其他棋盘上很有趣，因为深度神经网络通常需要大量高质量的数据来训练，而专家游戏可能不可用或难以生成。在本文中，我们研究了 Hex 中的神经迁移学习。我们表明，当仅使用板尺寸独立神经元时，通过在一个基本板尺寸上训练获得的神经网络可以有效地泛化 - 无需微调 - 更大或更小的多个目标板尺寸。当转移到更大的电路板尺寸时，微调提供了更快的学习和更好的性能。可转移网络的强度可以通过搜索放大：使用一个单一的神经网络模型训练来自基本棋盘大小的游戏，