Abstract
DeepMind’s recent spectacular success in using deep convolutional neural nets and machine learning to build superhuman level agents—e.g. for Atari games via deep Q-learning and for the game of Go via other deep Reinforcement Learning methods—raises many questions, including to what extent these methods will succeed in other domains. In this paper we consider DQL for the game of Hex: after supervised initializing, we use self-play to train NeuroHex, an 11-layer convolutional neural network that plays Hex on the 13 \(\times \) 13 board. Hex is the classic two-player alternate-turn stone placement game played on a rhombus of hexagonal cells in which the winner is whomever connects their two opposing sides. Despite the large action and state space, our system trains a Q-network capable of strong play with no search. After two weeks of Q-learning, NeuroHex achieves respective win-rates of 20.4% as first player and 2.1% as second player against a 1-s/move version of MoHex, the current ICGA Olympiad Hex champion. Our data suggests further improvement might be possible with more training time.
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Young, K., Vasan, G., Hayward, R. (2017). NeuroHex: A Deep Q-learning Hex Agent. In: Cazenave, T., Winands, M., Edelkamp, S., Schiffel, S., Thielscher, M., Togelius, J. (eds) Computer Games. CGW GIGA 2016 2016. Communications in Computer and Information Science, vol 705. Springer, Cham. https://doi.org/10.1007/978-3-319-57969-6_1
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DOI: https://doi.org/10.1007/978-3-319-57969-6_1
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