Design of Experiments for Reinforcement Learning, Christopher Gatti

Автор: Matthew Taylor
Название: Transfer in Reinforcement Learning Domains
ISBN: 3642018815 ISBN-13(EAN): 9783642018817
Издательство: Springer
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Цена: 158380.00 T
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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: Kathryn E. Merrick; Mary Lou Maher
Название: Motivated Reinforcement Learning
ISBN: 364210035X ISBN-13(EAN): 9783642100352
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended, virtual world.

Автор: Todd Hester
Название: TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
ISBN: 3319375105 ISBN-13(EAN): 9783319375106
Издательство: Springer
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Цена: 104480.00 T
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Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.

Автор: Todd Hester
Название: TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
ISBN: 3319011677 ISBN-13(EAN): 9783319011677
Издательство: Springer
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Цена: 130610.00 T
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Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.

Автор: Marco Wiering; Martijn van Otterlo
Название: Reinforcement Learning
ISBN: 364244685X ISBN-13(EAN): 9783642446856
Издательство: Springer
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Цена: 217670.00 T
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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: Shimon Whiteson
Название: Adaptive Representations for Reinforcement Learning
ISBN: 3642422314 ISBN-13(EAN): 9783642422317
Издательство: Springer
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Цена: 104480.00 T
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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: Lutz Frommberger
Название: Qualitative Spatial Abstraction in Reinforcement Learning
ISBN: 3642266002 ISBN-13(EAN): 9783642266003
Издательство: Springer
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Цена: 107130.00 T
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Описание: Reinforcement learning has evolved to tackle domains that are yet to be fully understood, or are too complex for a closed description. In this book the author investigates whether suitable abstraction methods can overcome the discipline`s deficiencies.

Автор: Matthew Taylor
Название: Transfer in Reinforcement Learning Domains
ISBN: 3642101860 ISBN-13(EAN): 9783642101861
Издательство: Springer
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Цена: 158380.00 T
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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.