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Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge
representation in reinforcement learning settings.
- Sales Rank: #2486963 in eBooks
- Published on: 2012-03-05
- Released on: 2012-03-05
- Format: Kindle eBook
From the Back Cover
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.
The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.
Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge
representation in reinforcement learning settings.
Most helpful customer reviews
1 of 5 people found the following review helpful.
Could not leave this fantastic book unreviewed.
By eldil
If you're reading this you already know it's 5 stars just for the fact that it exists. You can see the table of contents, it is awesome. As you can see for yourself, this is not just a collection of papers, but a nicely organized and well-digested monograph. I haven't read it much yet, but one small criticism - 'Options' isn't in the index, probably the indexers were not the authors and treated it merely as a common English word, but of course it does discuss options. It's pretty well appointed with diagrams and photos, some of them in color. The color ones are pleasant, but are a small proportion, and I'm not sure how much difference color makes, the choice to use color doesn't seem to follow much of a pattern. A final word - the softcover is plenty expensive, but the book is so thick I wish I'd bought the hardcover.
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