000 03538nam a22005295i 4500
001 978-3-030-59238-7
003 DE-He213
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007 cr nn 008mamaa
008 201121s2020 sz | s |||| 0|eng d
020 _a9783030592387
_9978-3-030-59238-7
024 7 _a10.1007/978-3-030-59238-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aPlaat, Aske.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aLearning to Play
_h[electronic resource] :
_bReinforcement Learning and Games /
_cby Aske Plaat.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXIII, 330 p. 111 illus., 72 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Intelligence and Games -- Reinforcement Learning -- Heuristic Planning -- Adaptive Sampling -- Function Approximation -- Self-Play -- Conclusion -- App. A, Deep Reinforcement Learning Environments -- App. B, Running Python -- App. C, Tutorial for the Game of Go -- App. D, AlphaGo Technical Details -- References -- List of Figures -- List of Tables -- List of Algorithms -- Index.
520 _aIn this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI). After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.
650 0 _aArtificial intelligence.
650 0 _aComputer games
_xProgramming.
650 0 _aGames.
650 1 4 _aArtificial Intelligence.
650 2 4 _aGame Development.
650 2 4 _aGames Studies.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030592370
776 0 8 _iPrinted edition:
_z9783030592394
776 0 8 _iPrinted edition:
_z9783030592400
856 4 0 _uhttps://doi.org/10.1007/978-3-030-59238-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c175435
_d175435