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Více informací o knize Discovering Hierarchy in Reinforcement Learning

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Anotace knihy

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.

Parametry knihy

Zařazení knihy Knihy v angličtině Society & social sciences Psychology Cognition & cognitive psychology

• Plný název: Discovering Hierarchy in Reinforcement Learning
• Podnázev: Automatic Modelling of Task-Hierarchies byMachines through Sense-Act Interactions with theirEnvironments
• Autor: Bernhard Hengst
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 200
• EAN: 9783639059243
• ISBN: 3639059247
• ID: 06815333
• Nakladatelství: VDM Verlag Dr. Mueller E.K.
• Hmotnost: 272 g
• Rozměry: 229 × 152 × 11 mm
• Datum vydání: 25. September 2008

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