Regulation of exploration for simple regret minimization in Monte-Carlo tree search

The application of multi-armed bandit (MAB) algorithms was a critical step in the development of Monte-Carlo tree search (MCTS). One example would be the UCT algorithm, which applies the UCB bandit algorithm. Various research has been conducted on applying other bandit algorithms to MCTS. Simple regret bandit algorithms, which aim to identify the optimal arm after a number of trials, have been of great interest in various fields in recent years. However, the simple regret bandit algorithm has the tendency to spend more time on sampling suboptimal arms, which may be a problem in the context of game tree search. In this research, we will propose combined confidence bounds, which utilize the characteristics of the confidence bounds of the improved UCB and UCB √· algorithms to regulate exploration for simple regret minimization in MCTS. We will demonstrate the combined confidence bounds bandit algorithm has better empirical performance than that of the UCB algorithm on the MAB problem. We will show that the combined confidence bounds MCTS (CCB-MCTS) has better performance over plain UCT on the game of 9 × 9 Go, and has shown good scalability. We will also show that the performance of CCB-MCTS can be further enhanced with the application of all-moves-as-first (AMAF) heuristic.