000			01430nam a2200277Ia 4500
008			211217s9999 xx 000 0 und d
020			_a9780262039246 (hb.)
082			_a006.31 _bSUT
100			_aSutton, Richard S. _92583
245		0	_aReinforcement learning : _ban introduction
250			_a2nd ed.
260			_aLondon _bThe MIT Press _c2020
300			_axxii, 526p.
440			_aAdaptive computation and machine learning series _98124
500			_ahttps://mitpress.mit.edu/books/reinforcement-learning-second-edition
520			_aReinforcement 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. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
650			_aReinforcement learning _9859
650			_aArtificial intelligence _9560
650			_aMultiarmed Bandits _98125
650			_aFinite Markov Decision Process _98126
650			_aMonte Carlo Methods _98127
650			_aTemporal Difference learning _98128
650			_aBootstrapping _98129
700			_aBarto, Andrew G. _98130
942			_cBK
999			_c7553 _d7553