Qualitative Spatial Abstraction in Reinforcement Learning, Lutz Frommberger

Автор: Marco Wiering; Martijn van Otterlo
Название: Reinforcement Learning
ISBN: 364244685X ISBN-13(EAN): 9783642446856
Издательство: Springer
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Цена: 32651.00 р.
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Описание: This book presents up-to-date information on the main contemporary sub-fields of reinforcement learning, including partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations.

Автор: Matthew Taylor
Название: Transfer in Reinforcement Learning Domains
ISBN: 3642101860 ISBN-13(EAN): 9783642101861
Издательство: Springer
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Цена: 23757.00 р.
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Описание: In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research.

Автор: Shimon Whiteson
Название: Adaptive Representations for Reinforcement Learning
ISBN: 3642422314 ISBN-13(EAN): 9783642422317
Издательство: Springer
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Цена: 15672.00 р.
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Описание: Presenting the main results of new algorithms for reinforcement learning, this book also introduces a novel method for devising input representations as well as presenting a way to find a minimal set of features sufficient to describe the agent`s current state.

Автор: Matthew Taylor
Название: Transfer in Reinforcement Learning Domains
ISBN: 3642018815 ISBN-13(EAN): 9783642018817
Издательство: Springer
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Цена: 23757.00 р.
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Описание: Reinforcement Learning Background.- Related Work.- Empirical Domains.- Value Function Transfer via Inter-Task Mappings.- Extending Transfer via Inter-Task Mappings.- Transfer between Different Reinforcement Learning Methods.- Learning Inter-Task Mappings.- Conclusion and Future Work.

Автор: Chao Wang; Gary D. Hachtel; Fabio Somenzi
Название: Abstraction Refinement for Large Scale Model Checking
ISBN: 1489993959 ISBN-13(EAN): 9781489993953
Издательство: Springer
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Цена: 15672.00 р.
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Описание: This book summarizes recent research on abstraction techniques for model checking large digital system. Considering the size of today`s digital systems and the capacity of state-of-the-art verification algorithms, abstraction is the only viable solution for the successful application of model checking techniques to industrial-scale designs.