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001433285 001__ 1433285
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001433285 020__ $$a9783030411886$$q(electronic bk.)
001433285 020__ $$a3030411885$$q(electronic bk.)
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001433285 0247_ $$a10.1007/978-3-030-41188-6$$2doi
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001433285 049__ $$aISEA
001433285 050_4 $$aQ325.6
001433285 08204 $$a006.31$$223
001433285 24500 $$aReinforcement learning algorithms :$$banalysis and applications /$$cBoris Belousov, Hany Abdulsamad, Pascal Klink, Simone Parisi, Jan Peters, editors.
001433285 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001433285 300__ $$a1 online resource (viii, 206 pages) :$$billustrations (some color)
001433285 336__ $$atext$$btxt$$2rdacontent
001433285 337__ $$acomputer$$bc$$2rdamedia
001433285 338__ $$aonline resource$$bcr$$2rdacarrier
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001433285 347__ $$bPDF
001433285 4901_ $$aStudies in computational intelligence,$$x1860-949X ;$$vvolume 883
001433285 504__ $$aIncludes bibliographical references.
001433285 5050_ $$aPrediction Error and Actor-Critic Hypotheses in the Brain -- Reviewing on-policy / o-policy critic learning in the context of Temporal Dierences and Residual Learning -- Reward Function Design in Reinforcement Learning -- Exploration Methods In Sparse Reward Environments -- A Survey on Constraining Policy Updates Using the KL Divergence -- Fisher Information Approximations in Policy Gradient Methods -- Benchmarking the Natural gradient in Policy Gradient Methods and Evolution Strategies -- Information-Loss-Bounded Policy Optimization -- Persistent Homology for Dimensionality Reduction -- Model-free Deep Reinforcement Learning Algorithms and Applications -- Actor vs Critic -- Bring Color to Deep Q-Networks -- Distributed Methods for Reinforcement Learning -- Model-Based Reinforcement Learning -- Challenges of Model Predictive Control in a Black Box Environment -- Control as Inference?
001433285 506__ $$aAccess limited to authorized users.
001433285 520__ $$aThis book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universitat Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.
001433285 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 26, 2021).
001433285 650_0 $$aReinforcement learning.
001433285 650_0 $$aAlgorithms.
001433285 650_6 $$aApprentissage par renforcement (Intelligence artificielle)
001433285 650_6 $$aAlgorithmes.
001433285 655_0 $$aElectronic books.
001433285 7001_ $$aBelousov, Boris,$$eeditor.
001433285 7001_ $$aAbdulsamad, Hany,$$eeditor.
001433285 7001_ $$aKlink, Pascal,$$eeditor.
001433285 7001_ $$aParisi, Simone,$$eeditor.
001433285 7001_ $$aPeters, Jan,$$d1976-$$eeditor.
001433285 77608 $$iPrint version:$$z3030411877$$z9783030411879$$w(OCoLC)1137813484
001433285 830_0 $$aStudies in computational intelligence ;$$vv. 883.$$x1860-949X
001433285 852__ $$bebk
001433285 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-41188-6$$zOnline Access$$91397441.1
001433285 909CO $$ooai:library.usi.edu:1433285$$pGLOBAL_SET
001433285 980__ $$aBIB
001433285 980__ $$aEBOOK
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001433285 983__ $$aOnline
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