Catalog

Control systems and reinforcement learning

Description:	xv, 435 pages :  illustrations (black and white) ; 27 cm.
Publisher:	Cambridge ; Cambridge University Press, 2022.
ISBN:	9781316511961 (hardback)
1 of 1 copy available at NOBLE (All Libraries). 1 of 1 copy available at Gordon College. Library Location Call number Status Gordon College Stack Level 5 Q 325.6 .M49 2022 Available
▸"A high school student can create deep Q-learning code to control her robot, without any ... understanding of the meaning of "deep" or "Q", or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning"-- Read More

Markov chains and stochastic stability

Description:	1 online resource (xxviii, 594 pages) : illustrations.
Publisher:	Cambridge ; Cambridge University Press, 2009.
Edition:	2nd ed.
Electronic resource	Access for Gordon College via EBSCOhost
▸Meyn and Tweedie is back! The bible on Markov chains in general state spaces has been brought up to ... date to reflect developments in the field since 1996 - many of them sparked by publication of the first edition. The pursuit of more efficient simulation algorithms for complex Markovian models, or algorithms for computation of optimal policies for controlled Markov models, has opened new directions for research on Markov chains. As a result, new applications have emerged across a wide range of topics including optimisation, statistics, and economics. New commentary and an epilogue by Sean Meyn summarise recent developments and references have been fully updated. This second edition reflects the same discipline and style that marked out the original and helped it to become a classic: proofs are rigorous and concise, the range of applications is broad and knowledgeable, and key ideas are accessible to practitioners with limited mathematical background. Read More

Handbook of Markov decision processes : methods and applications

Description:	viii, 565 p. : ill. ; 25 cm.
Publisher:	Boston : Kluwer Academic Publishers, c2002.
ISBN:	0792374592 (acid-free paper)
1 of 1 copy available at NOBLE (All Libraries). 1 of 1 copy available at Gordon College. Library Location Call number Status Gordon College Stack Level 5 QA 279.4 .H35 2002 Available