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Relevant bibliographies by topics / Reinforcement / Books

Books on the topic 'Reinforcement'

To see the other types of publications on this topic, follow the link: Reinforcement.

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2024

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1

Association, British Cement, ed. Reinforcement. Crowthorne: British Cement Association, 1993.

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2

Sutton, Richard S. Reinforcement Learning. Boston, MA: Springer US, 1992.

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3

Wiering, Marco, and Martijn van Otterlo, eds. Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3.

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4

Sutton, Richard S., ed. Reinforcement Learning. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5.

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5

Lorenz, Uwe. Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61651-2.

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6

Nandy, Abhishek, and Manisha Biswas. Reinforcement Learning. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3285-9.

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7

S, Sutton Richard, ed. Reinforcement learning. Boston: Kluwer Academic Publishers, 1992.

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8

Li, Jinna, Frank L. Lewis, and Jialu Fan. Reinforcement Learning. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28394-9.

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9

Lorenz, Uwe. Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68311-8.

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10

Merrick, Kathryn, and Mary Lou Maher. Motivated Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89187-1.

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11

Kohjiya, Shinzo, Atsushi Kato, and Yuko Ikeda. Reinforcement of Rubber. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3789-9.

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12

Dong, Hao, Zihan Ding, and Shanghang Zhang, eds. Deep Reinforcement Learning. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4095-0.

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13

Sewak, Mohit. Deep Reinforcement Learning. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8285-7.

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14

Roozen, H. G., R. J. Meyers, and J. E. Smith. Community Reinforcement Approach. Houten: Bohn Stafleu van Loghum, 2013. http://dx.doi.org/10.1007/978-90-313-9756-3.

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15

Stark, Scott Hunter. Live sound reinforcement. Ocean Gate, NJ: Tech-Access Pub., 1993.

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16

Petersen, Dan. Safe behavior reinforcement. Goshen, N.Y: Aloray, 1989.

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17

Davis, Gary. The sound reinforcement handbook. 2nd ed. Milwaukee, Wis: Hal Leonard Publishing, 1991.

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18

Szepesvári, Csaba. Algorithms for Reinforcement Learning. Cham: Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01551-9.

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19

Lorenz, Uwe. Reinforcement Learning From Scratch. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09030-1.

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20

Ahlawat, Samit. Reinforcement Learning for Finance. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-8835-1.

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21

Norihiko, Miura, and Yamanouchi Toyotoshi, eds. Geosynthetic reinforcement in foundations. Barking: Elsevier Science Publishers, 1990.

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22

Jewell, R. A. Soil reinforcement with geotextiles. London: CIRIA and Thomas Telford, 1996.

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23

V, Sudakov K., Ganten D. 1941-, and Nikolov Nikola A, eds. Reinforcement in functional systems. New York: Gordon and Breach, 1991.

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24

Sutton, Richard S. Reinforcement learning: An introduction. Cambridge, Mass: MIT Press, 1998.

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25

Jewell, R. A. Soil reinforcement with geotextiles. London: Construction Industry Research and Information Association, 1996.

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26

Ochawa. EARTH REINFORCEMENT PRACTICE-V1 (Earth Reinforcement). Routledge, 2004.

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27

Ochiai. EARTH REINFORCEMENT V1 (Earth Reinforcement Practice). Taylor & Francis, 1996.

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28

Weber, Cornelius, Mark Elshaw, and Norbert Michael, eds. Reinforcement Learning. I-Tech Education and Publishing, 2008. http://dx.doi.org/10.5772/54.

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29

Polymer Reinforcement. ChemTec Publishing, 1995.

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30

Martens, Koen. Negative Reinforcement. Koen Martens, 2021.

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31

Staff, Moulton Company. A+ Reinforcement. Pearson Education, Limited, 2005.

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32

Edwards, W. Jini Reinforcement. Pearson Education, Limited, 1999.

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33

KOOB, LEWIS, MEYER, and PAUL. Alcohol Reinforcement. Birkhauser, 1990.

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34

Magazine Reinforcement. Standard Publishing Company, 1994.

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35

Anonyma. Reinforcement Textbook. Heinemann Educational Books - Primary Division, 1992.

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36

Trevorrow, A. Steel Reinforcement. Longman Group United Kingdom, 1985.

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37

Reinforcement Textbook. Heinemann Educational Books - Primary Division, 1991.

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38

[ MEI ] AndrewG.Barto [ JIA ] RichardS.Sutton . Reinforcement learning. Electronic Industry Press, 2019.

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39

Stellar, James R., Michael L. Commons, Russell M. Church, and Allan R. Wagner. Biological Determinants of Reinforcement: Biological Determinates of Reinforcement. Taylor & Francis Group, 2014.

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40

Stellar, James R., Michael L. Commons, Russell M. Church, and Allan R. Wagner. Biological Determinants of Reinforcement: Biological Determinates of Reinforcement. Taylor & Francis Group, 2014.

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41

Stellar, James R., Michael L. Commons, Russell M. Church, and Allan R. Wagner. Biological Determinants of Reinforcement: Biological Determinates of Reinforcement. Taylor & Francis Group, 2014.

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42

Stellar, James R., Michael L. Commons, Russell M. Church, and Allan R. Wagner. Biological Determinants of Reinforcement: Biological Determinates of Reinforcement. Taylor & Francis Group, 2014.

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43

Looi, Daniel TW, Ray KL Su, and Emad Gad. A State-of-the-Art Guide for Post-Installed Reinforcement. Sunway University Press, 2022. http://dx.doi.org/10.55846/9789675492747.

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Abstract:

A State-of-the-Art Guide for Post-Installed Reinforcement provides comprehensive coverage on installation, design, and assesment guidelines for post-installed reinforcements. Previously published in Hong Kong, this Malaysian edition includes new EOTA technical reports and European Assessment Documents, fundamentals for post-installed reinforcements, design proposals, and unique design examples, all of which are specifically tailored to the Malaysian context.

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44

Sugiyama, Masashi. Statistical Reinforcement Learning. Taylor & Francis Group, 2020.

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45

Sugiyama, Masashi. Statistical Reinforcement Learning. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b18188.

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46

Gureckis, Todd M., and Bradley C. Love. Computational Reinforcement Learning. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.5.

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Abstract:

Reinforcement learning (RL) refers to the scientific study of how animals and machines adapt their behavior in order to maximize reward. The history of RL research can be traced to early work in psychology on instrumental learning behavior. However, the modern field of RL is a highly interdisciplinary area that lies that the intersection of ideas in computer science, machine learning, psychology, and neuroscience. This chapter summarizes the key mathematical ideas underlying this field including the exploration/exploitation dilemma, temporal-difference (TD) learning, Q-learning, and model-based versus model-free learning. In addition, a broad survey of open questions in psychology and neuroscience are reviewed.

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47

Polymer grid reinforcement. Thomas Telford Ltd, 1985. http://dx.doi.org/10.1680/pgr.02425.

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48

Ahnert, Wolfgang, and Frank Steffen. Sound Reinforcement Engineering. CRC Press, 2000. http://dx.doi.org/10.1201/9781482267631.

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49

Stark, Scott. Live Sound Reinforcement. ArtistPro, 1996.

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50

Jones, Ralph, and Gary Davis. Sound Reinforcement Handbook. Leonard Corporation, Hal, 1988.

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