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Online learning algorithms for reinforcement learning with function approximation

Robards, Matthew Walters

Description

Reinforcement learning deals with the problem of sequential decision making in uncertain stochastic environments. In this thesis I deal with agents who attempt to solve the reinforcement learning problem online and in real-time. This presents experimental challenges for which I introduce novel kernelised algorithms. Kernel algorithms are very useful in reinforcement learning settings as they enable learning in situations where a very high-dimensional or hand engineered feature vector would...[Show more]

CollectionsOpen Access Theses
Date published: 2011
Type: Thesis (PhD)
URI: http://hdl.handle.net/1885/150825
DOI: 10.25911/5d51538ad356e
Access Rights: Open Access

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