000			a
999			_c34489 _d34489
008			250818b xxu||||| |||| 00| 0 eng d
020			_a9780262039246 _c(hbk)
082			_a006.3 _bSUT
100			_aSutton, Richard S.
245			_a Reinforcement learning : an introduction
250			_a2nd ed.
260			_bMIT Press, _c2018 _aCambridge, Massachusetts :
300			_axxii, 526 p. ; _bill., _c24 cm.
365			_b9650.00 _c₹ _d01
490			_a Adaptive computation and machine learning series
504			_aIncludes bibliographical references and index.
520			_aIn Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.--Jacket. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms.-- Provided by publisher.
650			_aArtificial Intelligence
650			_aBellman equation
650			_aDynamic programming
650			_a Function approximation
650			_aMonte Carlo methods
650			_aMarkov property
650			_aQ-learning
700			_aBarto, Andrew G.
942			_2ddc _cBK