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Efficient Reinforcement Learning in High Dimensional Domains (Paperback)
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Efficient Reinforcement Learning in High Dimensional Domains (Paperback)
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This book presents development of efficient reinforcement learning
methods in a postgraduate research. A reinforcement learning agent
tries every state-action pair to find the optimal policy without
prior knowledge about the domain. In large domains visiting every
state-action pair is not feasible by an agent, therefore standard
reinforcement learning approach is not applicable in solving many
real world problems. Three new methods are proposed to make the
learning efficient according to the characteristics of the
problems: Task-Oriented Reinforcement Learning reduces the problem
size by viewing it from the task's viewpoint that clarifies task
relevant state variables. Symmetrical-Actions Reinforcement Leaning
reduces the size of a learning problem by exploiting partial
symmetry over action relevant state variables and representing
actions values by a single function. Coordinated Multiagent
Reinforcement Learning technique uses coordinator-agent hierarchy
to keep the size of individual learning problems small. Depending
on problem characteristics all or any of these methods can be
applied to solve a problem efficiently using reinforcement
learning.
General
Imprint: |
Lap Lambert Academic Publishing
|
Country of origin: |
Germany |
Release date: |
December 2011 |
First published: |
December 2011 |
Authors: |
MD Abdus Samad Kamal
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Dimensions: |
229 x 152 x 6mm (L x W x T) |
Format: |
Paperback - Trade
|
Pages: |
96 |
ISBN-13: |
978-3-8465-5571-2 |
Categories: |
Books >
Computing & IT >
General theory of computing >
General
|
LSN: |
3-8465-5571-1 |
Barcode: |
9783846555712 |
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