000			a
999			_c32157 _d32157
008			231101b xxu||||| |||| 00| 0 eng d
020			_a9781316511961
082			_a006.3 _bMEY
100			_aMeyn, S. P.
245			_aControl systems and reinforcement learning
260			_bCambridge University Press, _c2022 _aCambridge :
300			_axv, 435 p. ; _bill., _c27 cm.
365			_b49.99 _cGBP _d107.60
504			_aIncludes bibliographical references and index.
520			_aA 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.
650			_aControl theory
650			_aMathematical optimization
650			_aComputer vision
650			_aPattern recognition
650			_aStochastic Processes
650			_a Econometrics
650			_a Cost function
650			_aContro theory
650			_aApproximation
942			_2ddc _cBK