Reinforcement Learning

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control  eBooks & eLearning

Posted by Grev27 at Nov. 22, 2015
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Frank L. Lewis, Derong Liu, "Reinforcement Learning and Approximate Dynamic Programming for Feedback Control"
English | ISBN: 111810420X | 2012 | EPUB | 648 pages | 7,2 MB
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (Repost)

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control By Frank L. Lewis, Derong Liu
2013 | 648 Pages | ISBN: 111810420X | PDF | 45 MB

Reinforcement Learning: An Introduction  

Posted by step778 at Sept. 16, 2015
Reinforcement Learning: An Introduction

Richard S. Sutton, Andrew G. Barto, "Reinforcement Learning: An Introduction"
1998 | pages: 331 | ISBN: 0262193981 | PDF | 2,3 mb
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains [Repost]

Todd Hester - TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
Published: 2013-07-04 | ISBN: 3319011677, 3319011693 | PDF | 165 pages | 5.94 MB
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (repost)

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control by Frank L. Lewis
English | Dec 26, 2012 | ISBN: 111810420X | 648 Pages | PDF | 44 MB

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games.
Statistical Reinforcement Learning: Modern Machine Learning Approaches

Statistical Reinforcement Learning: Modern Machine Learning Approaches by Masashi Sugiyama
2015 | ISBN: 1439856893 | English | 206 pages | True PDF | 7 MB
Statistical Reinforcement Learning: Modern Machine Learning Approaches

Statistical Reinforcement Learning: Modern Machine Learning Approaches by Masashi Sugiyama
English | Mar 26, 2015 | ISBN: 1439856893 | 208 Pages | AZW3/MOBI/EPUB/PDF (conv) | 31 MB

Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and game players have been successfully explored in recent years.
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains (repost)

TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains by Todd Hester
English | ISBN: 3319011677 | 2013 | 200 pages | PDF | 6 MB

Design of Experiments for Reinforcement Learning  

Posted by advisors at March 4, 2015
Design of Experiments for Reinforcement Learning

Design of Experiments for Reinforcement Learning By Christopher Gatti
2015 | 208 Pages | ISBN: 3319121960 | PDF | 6 MB
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (repost)

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control by Frank L. Lewis
English | Dec 26, 2012 | ISBN: 111810420X | 648 Pages | PDF | 44 MB

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.