  The combination of Monte-Carlo tree search (MCTS) with deep reinforcement
learning has led to significant advances in artificial intelligence. However,
AlphaZero, the current state-of-the-art MCTS algorithm, still relies on
handcrafted heuristics that are only partially understood. In this paper, we
show that AlphaZero's search heuristics, along with other common ones such as
UCT, are an approximation to the solution of a specific regularized policy
optimization problem. With this insight, we propose a variant of AlphaZero
which uses the exact solution to this policy optimization problem, and show
experimentally that it reliably outperforms the original algorithm in multiple
domains.
