000 | 03538nam a22005295i 4500 | ||
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001 | 978-3-030-59238-7 | ||
003 | DE-He213 | ||
005 | 20240423125216.0 | ||
007 | cr nn 008mamaa | ||
008 | 201121s2020 sz | s |||| 0|eng d | ||
020 |
_a9783030592387 _9978-3-030-59238-7 |
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024 | 7 |
_a10.1007/978-3-030-59238-7 _2doi |
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050 | 4 | _aTA347.A78 | |
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_aUYQ _2bicssc |
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_aCOM004000 _2bisacsh |
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_aUYQ _2thema |
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_a006.3 _223 |
100 | 1 |
_aPlaat, Aske. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIII, 330 p. 111 illus., 72 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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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. |
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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 | ||
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