The effect of simulation bias on action selection in Monte Carlo Tree Search

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dc.contributor.authorJames, Steven Doron
dc.date.accessioned2017-01-19T05:42:04Z
dc.date.available2017-01-19T05:42:04Z
dc.date.issued2016
dc.descriptionA dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Science. August 2016.en_ZA
dc.description.abstractMonte Carlo Tree Search (MCTS) is a family of directed search algorithms that has gained widespread attention in recent years. It combines a traditional tree-search approach with Monte Carlo simulations, using the outcome of these simulations (also known as playouts or rollouts) to evaluate states in a look-ahead tree. That MCTS does not require an evaluation function makes it particularly well-suited to the game of Go — seen by many to be chess’s successor as a grand challenge of artificial intelligence — with MCTS-based agents recently able to achieve expert-level play on 19×19 boards. Furthermore, its domain-independent nature also makes it a focus in a variety of other fields, such as Bayesian reinforcement learning and general game-playing. Despite the vast amount of research into MCTS, the dynamics of the algorithm are still not yet fully understood. In particular, the effect of using knowledge-heavy or biased simulations in MCTS still remains unknown, with interesting results indicating that better-informed rollouts do not necessarily result in stronger agents. This research provides support for the notion that MCTS is well-suited to a class of domain possessing a smoothness property. In these domains, biased rollouts are more likely to produce strong agents. Conversely, any error due to incorrect bias is compounded in non-smooth domains, and in particular for low-variance simulations. This is demonstrated empirically in a number of single-agent domains.en_ZA
dc.description.librarianLG2017en_ZA
dc.format.extentOnline resource (69 leaves)
dc.identifier.citationJames, Steven Doron (2016) The effect of simulation bias on action selection in Monte Carlo Tree Search, University of Witwatersrand, Johannesburg, <http://wiredspace.wits.ac.za/handle/10539/21673>
dc.identifier.urihttp://hdl.handle.net/10539/21673
dc.language.isoenen_ZA
dc.subject.lcshSimulation methods
dc.subject.lcshMonte Carlo method
dc.titleThe effect of simulation bias on action selection in Monte Carlo Tree Searchen_ZA
dc.typeThesisen_ZA
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