  The combination of Monte-Carlo Tree Search (MCTS) and deep reinforcement
learning is state-of-the-art in two-player perfect-information games. In this
paper, we describe a search algorithm that uses a variant of MCTS which we
enhanced by 1) a novel action value normalization mechanism for games with
potentially unbounded rewards (which is the case in many optimization
problems), 2) defining a virtual loss function that enables effective search
parallelization, and 3) a policy network, trained by generations of self-play,
to guide the search. We gauge the effectiveness of our method in "SameGame"---a
popular single-player test domain. Our experimental results indicate that our
method outperforms baseline algorithms on several board sizes. Additionally, it
is competitive with state-of-the-art search algorithms on a public set of
positions.
