Monte-Carlo Simulation Balancing in Practice
Simulation balancing is a new technique to tune parameters of a playout policy for a Monte-Carlo game-playing program. So far, this algorithm had only been tested in a very artificial setting: it was limited to $5\times5$ and $6\times6$ Go, and required a stronger external program that served as a supervisor. In this paper, the effectiveness of simulation balancing is demonstrated in a more realistic setting. A state-of-the-art program, \Erica, learned an improved playout policy on the $9\times9$ board, without requiring any external expert to provide position evaluations. Evaluations were collected by letting the program analyze positions by itself. The previous version of \Erica\ learned pattern weights with the minorization-maximization algorithm. Thanks to simulation balancing, its playing strength was improved from a winning rate of 69\% to 78\% against \Fuego~0.4.