000 03369nam a22005295i 4500
001 978-3-031-01551-9
003 DE-He213
005 20240730164105.0
007 cr nn 008mamaa
008 220601s2010 sz | s |||| 0|eng d
020 _a9783031015519
_9978-3-031-01551-9
024 7 _a10.1007/978-3-031-01551-9
_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 _aSzepesvári, Csaba.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982051
245 1 0 _aAlgorithms for Reinforcement Learning
_h[electronic resource] /
_cby Csaba Szepesvári.
250 _a1st ed. 2010.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2010.
300 _aXIII, 89 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aMarkov Decision Processes -- Value Prediction Problems -- Control -- For Further Exploration.
520 _aReinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_982052
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
710 2 _aSpringerLink (Online service)
_982053
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000218
776 0 8 _iPrinted edition:
_z9783031004230
776 0 8 _iPrinted edition:
_z9783031026799
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_982054
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01551-9
912 _aZDB-2-SXSC
942 _cEBK
999 _c85289
_d85289