Hyper-parameter Optimization for Monte Carlo Tree Search using Self-play
- 한국스마트미디어학회
- 스마트미디어저널
- Vol9, No.4
- : KCI등재후보
- 2020.12
- 36 - 43 (8 pages)
The Monte Carlo tree search (MCTS) is a popular method for implementing an intelligent game program. It has several hyper-parameters that require an optimization for showing the best performance. Due to the stochastic nature of the MCTS, the hyper-parameter optimization is difficult to solve. This paper uses the self-playing capability of the MCTS-based game program for optimizing the hyper-parameters. It seeks a winner path over the hyper-parameter space while performing the self-play. The top-q longest winners in the winner path compete for the final winner. The experiment using the 15-15-5 game (Omok in Korean name) showed a promising result.
I. INTRODUCTION
II. MCTS ALGORITHM AND HYPER-PARAMETERS
III. ALGORITHM FOR HYPER-PARAMETER OPTIMIZATION OF MCTS
IV. m-n-k GAME AS A TESTBED
V. EXPERIMENTS
VI. CONCLUSIONS
REFERENCES