01638nam\a2200289\a\45000010006000000050017000060080041000230200042000640400033001060820023001391000033001622450088001952500020002832600035003033000060003384900053003985040067004515200404005185260017009225410017009396500028009567000031009849420012010159990017010279520152010449520152011965789320260707140653.0260707s2020    maua     b    001 0 eng    a9780262039246qhardcover : alk. paper  aDLCbengcDLCerdadBD-DhIUB00a006.31223bS9671r1 aSutton, Richard S.,eauthor.10aReinforcement learning :ban introduction /cRichard S. Sutton and Andrew G. Barto.  aSecond edition.  aEngland:bThe  MIT Pressc2020  axxii, 526 pages :billustrations (some color) ;c24 cm.0 aAdaptive computation and machine learning series  aIncludes bibliographical references (pages 481-518) and index.  a"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."--  aCSEbpslREF  aOmni concept 0aReinforcement learning.1 aBarto, Andrew G.,eauthor.  2ddccBK  c57893d58067  00102ddc4070aIUBLbIUBLcGENd2026-06-23ePURCHASEg10557.00h2020l0o006.31 S9671rp029882r2026-07-07 14:08:00t01v21114.00w2026-06-23yBK  00102ddc4071aIUBLbIUBLcREFd2026-06-23ePURCHASEg10557.00h2020l0o006.31 S9671rp029883r2026-07-07 14:08:00t02v21114.00w2026-06-23yBK