Kniha Discovering Hierarchy in Reinforcement Learning Bernhard Hengst

Discovering Hierarchy in Reinforcement Learning

Automatic Modelling of Task-Hierarchies byMachines through Sense-Act Interactions with theirEnvironments

Jazyk: Angličtina
Vazba: Brožovaná
Dostupnost: Skladem u dodavatele
Odesíláme za 14-21 dnů
1 583
We are relying more and more on machines to performtasks that were previously the sole domain ofhuma...

Informace o knize

Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2008
Stránek
200
EAN
9783639059243
ISBN
3639059247
Enbook ID
06815333
Hmotnost
272
Rozměry
152 x 229 x 11

Kompletní popis

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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