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Optimistic agents are asymptotically optimal

Sunehag, Peter; Hutter, Marcus

Description

We use optimism to introduce generic asymptotically optimal reinforcement learning agents. They achieve, with an arbitrary finite or compact class of environments, asymptotically optimal behavior. Furthermore, in the finite deterministic case we provide finite error bounds.

CollectionsANU Research Publications
Date published: 2012
Type: Conference paper
URI: http://hdl.handle.net/1885/71407
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI: 10.1007/978-3-642-35101-3_2

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