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Self-adjusting reinforcement learning

We present a variant of the Q-learning algorithm with automatic control of the exploration rate by a competition scheme. The theoretical approach is accompanied by systematic simulations of a chaos control
task. Finally, we give interpretations of the algorithm in the context of computational ecology and neural networks.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:32411
Date10 December 2018
CreatorsDer, Ralf, Herrmann, Michael
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text
Rightsinfo:eu-repo/semantics/openAccess

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