Focused depth-first proof number search using convolutional neural networks for the game of Hex

Abstract

Proof Number search (PNS) is an effective algorithm for searching theoretical values on games with non-uniform branching factors. Focused depth-first proof number search (FDFPN) with dynamic widening was proposed for Hex where the branching factor is nearly uniform. However, FDFPN is fragile to its heuristic move ordering function. The recent advances of Convolutional Neural Networks (CNNs) have led to considerable progress in game playing. We investigate how to incorporate the strength of CNNs into solving, with application to the game of Hex. We describe FDFPN-CNN, a new focused DFPN search that uses convolutional neural networks. FDFPN-CNN integrates two CNNs trained from games played by expert players. The value approximation CNN provides reliable information for defining the widening size by estimating the value of the node to expand, while the policy CNN selects promising children nodes to the search. On 8x8 Hex, experimental results show FDFPN-CNN performs notably better than FDFPN, suggesting a promising direction for better solving Hex positions where learning from strong players is possible.