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Transfer in Reinforcement Learning Domains (Studies in Computational Intelligence) by Matthew E. Taylor

In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge. The key insight behind "transfer learning" is that generalization may occur not only within tasks, but also across tasks. While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research.

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	Study Recent Advances in Reinforcement Learning Recent Advances in Reinforcement Learning: 8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30-July 3, 2008283 pages \| Springer; 1 edition (December 1, 2008) \| ISBN-10: 3540897216 \| PDF \| 10 MbThis book constitutes revised ...
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	Study Recent Advances in Reinforcement Learning Recent Advances in Reinforcement Learning: 8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30-July 3, 2008283 pages \| Springer; 1 edition (December 1, 2008) \| ISBN-10: 3540897216 \| PDF \| 10 MbThis book constitutes revised ...
	Study Motivated Reinforcement Learning: Curious Characters for Multiuser Games Motivated Reinforcement Learning: Curious Characters for Multiuser GamesSpringer \| ISBN: 3540891862 \| 2009-07-01 \| PDF \| 206 pages \| 9 Mb Motivated learning is an emerging research field in artificial intelligence and cognitive modelling. C ...
	Science/Engineering Adaptive Brain: Cognition, Learning, Reinforcement and Rhythm Pt. 1 by Stephen Grossberg Adaptive Brain: Cognition, Learning, Reinforcement and Rhythm Pt. 1 by Stephen GrossbergPublisher: Elsevier Science \| December 1986 \| ISBN: 0444701176 \| Pages: 518 \| PDF \| 9.3 MBThese books bring together recent neural models of vision, spe ...
	Technical Reinforcement Learning: An Introduction Reinforcement learning is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In this book, Richard Sutton and Andrew Barto p ...
	Technical Reinforcement Learning The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too com ...
	Programming Reinforcement Learning: Theory and Applications Cornelius Weber, Mark Elshaw and Norbert Michael Mayer “Reinforcement Learning: Theory and Applications" I-Tech Education and Publishing, Vienna, Austria \| 2008-01 \| ISBN: 9783902613141 \| 424 pages \| PDF \| 11,7 MB Brains rule the world ...

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