Ensemble Monte-Carlo planning : an empirical study Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/k930c117s

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  • Monte-Carlo planning algorithms such as UCT make decisions at each step 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 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 large scale empirical study of ensemble Monte-Carlo planning using the UCT algorithm in a set of five additional diverse and challenging 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 sequential time models. Our results show that ensembles are an effective way to improve performance given a parallel model, can significantly reduce space requirements and in some cases may improve performance in a sequential model. Additionally, from our work we produced an open-source planning library.
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