Monte-Carlo Go Reinforcement Learning Experiments

Abstract— This paper describes experiments using reinforcement learning techniques to compute pattern urgencies used during simulations performed in a Monte-Carlo Go architecture. Currently, Monte-Carlo is a popular technique for computer Go. In a previous study, Monte-Carlo was associated with domain-dependent knowledge in the Go-playing program Indigo. In 2003, a 3x3 pattern database was built manually. This paper explores the possibility of using reinforcement learning to automatically tune the 3x3 pattern urgencies. On 9x9 boards, within the Monte-Carlo architecture of Indigo, the result obtained by our automatic learning experiments is better than the manual method by a 3-point margin on average, which is satisfactory. Although the current results are promising on 19x19 boards, obtaining strictly positive results with such a large size remains to be done.

• Type: Conference
• Year: 2006
• Where: CIG
• Authors: Bruno Bouzy, Guillaume Chaslot
• Keywords: Applied Computing, CIG 2006, Monte-Carlo, Monte-Carlo Go Architecture, Reinforcement Learning