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Discovering Hierarchy in Reinforcement Learning

Hengst:Discovering Hierarchy in Reinfor
Autor: Bernhard Hengst
Verfügbarkeit: Auf Lager.
Artikelnummer: 1359515
ISBN / EAN: 9783639059243

Verfügbarkeit: sofort lieferbar

68,00 €
Inkl. MwSt. , zzgl. Versandkosten


  • Autor:
  • Verlag: VDM Verlag Dr. Müller
  • ISBN / EAN: 9783639059243
  • Bindung: Taschenbuch


We are relying more and more on machines to performtasks that were previously the sole domain ofhumans. There is a need to make machines more self-adaptable and for them to set their own sub-goals.Designing machines that can make sense of the worldthey inhabit is still an open research problem.Fortunately many complex environments exhibitstructure that can be modelled as an inter-relatedset of subsystems. Subsystems are often repetitivein time and space and reoccur many times ascomponents of different tasks. A machine may be ableto learn how to tackle larger problems if it cansuccessfully find and exploit this repetition.Evidence suggests that a bottom up approach, thatrecursively finds building-blocks at one level ofabstraction and uses them at the next level, makeslearning in many complex environments tractable.This book describes a machine learning algorithmcalled HEXQ that automatically discovershierarchical structure in its environment purelythrough sense-act interactions, setting its own sub-goals and solving decision problems usingreinforcement learning.

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