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001447625 001__ 1447625
001447625 003__ OCoLC
001447625 005__ 20230310004126.0
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001447625 008__ 220621s2022\\\\si\\\\\\ob\\\\001\0\eng\d
001447625 019__ $$a1330690599$$a1330934081
001447625 020__ $$a9789811906381$$q(electronic bk.)
001447625 020__ $$a9811906386$$q(electronic bk.)
001447625 020__ $$z9811906378
001447625 020__ $$z9789811906374
001447625 0247_ $$a10.1007/978-981-19-0638-1$$2doi
001447625 035__ $$aSP(OCoLC)1331408971
001447625 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dOCLCF$$dN$T$$dUKAHL$$dOCLCQ
001447625 049__ $$aISEA
001447625 050_4 $$aQ325.6
001447625 08204 $$a006.3/1$$223/eng/20220621
001447625 1001_ $$aPlaat, Aske.
001447625 24510 $$aDeep reinforcement learning /$$cAske Plaat.
001447625 264_1 $$aSingapore :$$bSpringer,$$c2022.
001447625 300__ $$a1 online resource (1 volume) :$$billustrations (black and white, and color).
001447625 336__ $$atext$$btxt$$2rdacontent
001447625 337__ $$acomputer$$bc$$2rdamedia
001447625 338__ $$aonline resource$$bcr$$2rdacarrier
001447625 504__ $$aIncludes bibliographical references and index.
001447625 5050_ $$a1. Introduction -- 2. Tabular Value-Based Methods -- 3. Approximating the Value Function -- 4. Policy-Based Methods -- 5. Model-Based Methods -- 6. Two-Agent Reinforcement Learning -- 7. Multi-Agent Reinforcement Learning -- 8. Hierarchical Reinforcement Learning -- 9. Meta Learning -- 10. Further Developments -- A. Deep Reinforcement Learning Suites -- B. Deep Learning -- C. Mathematical Background.
001447625 506__ $$aAccess limited to authorized users.
001447625 520__ $$aDeep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the worlds leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.
001447625 588__ $$aDescription based on print version record.
001447625 650_0 $$aReinforcement learning.
001447625 650_0 $$aArtificial intelligence.
001447625 650_0 $$aHuman-computer interaction.
001447625 655_0 $$aElectronic books.
001447625 77608 $$iPrint version:$$aPLAAT, ASKE.$$tDEEP REINFORCEMENT LEARNING.$$d[Place of publication not identified] : SPRINGER VERLAG, SINGAPOR, 2022$$z9811906378$$w(OCoLC)1294283891
001447625 852__ $$bebk
001447625 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-0638-1$$zOnline Access$$91397441.1
001447625 909CO $$ooai:library.usi.edu:1447625$$pGLOBAL_SET
001447625 980__ $$aBIB
001447625 980__ $$aEBOOK
001447625 982__ $$aEbook
001447625 983__ $$aOnline
001447625 994__ $$a92$$bISE