Parallelization of Information Set Monte Carlo Tree Search

Process parallelization is more important than ever, as most modern hardware contains multiple processors and advanced multi-threading capability. This paper presents an analysis of the parallel behaviour of Information Set Monte Carlo Tree Search and the Upper Confidence Bounds for Trees (UCT) variant of MCTS, and certain parallelization techniques (specifically Tree Parallelization) have different effects upon ISMCTS and Plain UCT. The paper presents a study of the relative effectiveness of different types of parallelization, including Root, Tree, Tree with Virtual Loss, and Leaf.