  We consider the popular tree-based search strategy within the framework of
reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of
finite-horizon Markov decision process. We propose a dynamic sampling tree
policy that efficiently allocates limited computational budget to maximize the
probability of correct selection of the best action at the root node of the
tree. Experimental results on Tic-Tac-Toe and Gomoku show that the proposed
tree policy is more efficient than other competing methods.
