Ensemble Monte-Carlo Planning: An Empirical Study

Abstract

Monte-Carlo planning algorithms, such as UCT, select actions at each decision epoch by intelligently expanding a single search tree given the available time and then selecting the best root action. Recent work has provided evidence that it can be advantageous to instead construct an ensemble of search trees and to make a decision according to a weighted vote. However, these prior investigations have only considered the application domains of Go and Solitaire and were limited in the scope of ensemble configurations considered. In this paper, we conduct a more exhaustive empirical study of ensemble Monte-Carlo planning using the UCT algorithm in a set of six additional domains. In particular, we evaluate the advantages of a broad set of ensemble configurations in terms of space and time efficiency in both parallel and singlecore models. Our results demonstrate that ensembles are an effective way to improve performance per unit time given a parallel time model and performance per unit space in a single-core model. However, contrary to prior isolated observations, we did not find significant evidence that ensembles improve performance per unit time in a single-core model.