  We consider Monte-Carlo Tree Search (MCTS) applied to Markov Decision
Processes (MDPs) and Partially Observable MDPs (POMDPs), and the well-known
Upper Confidence bound for Trees (UCT) algorithm. In UCT, a tree with nodes
(states) and edges (actions) is incrementally built by the expansion of nodes,
and the values of nodes are updated through a backup strategy based on the
average value of child nodes. However, it has been shown that with enough
samples the maximum operator yields more accurate node value estimates than
averaging. Instead of settling for one of these value estimates, we go a step
further proposing a novel backup strategy which uses the power mean operator,
which computes a value between the average and maximum value. We call our new
approach Power-UCT, and argue how the use of the power mean operator helps to
speed up the learning in MCTS. We theoretically analyze our method providing
guarantees of convergence to the optimum. Finally, we empirically demonstrate
the effectiveness of our method in well-known MDP and POMDP benchmarks, showing
significant improvement in performance and convergence speed w.r.t. state of
the art algorithms.
