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1

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

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2

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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3

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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4

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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5

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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6

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

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7

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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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

Xiao, Zhiqing. Reinforcement Learning. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-4933-3.

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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

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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12

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

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13

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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14

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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15

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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16

Reynolds, Stuart Ian. Reinforcement learning with exploration. Birmingham: University of Birmingham, 2002.

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17

Szepesvári, Csaba. Algorithms for reinforcement learning. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2010.

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18

Ris-Ala, Rafael. Fundamentals of Reinforcement Learning. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37345-9.

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19

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

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20

Pack, Kaelbling Leslie, ed. Recent advances in reinforcement learning. Boston: Kluwer Academic, 1996.

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21

Sanghi, Nimish. Deep Reinforcement Learning with Python. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6809-4.

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22

Taylor, Matthew E. Transfer in Reinforcement Learning Domains. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01882-4.

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23

Beysolow II, Taweh. Applied Reinforcement Learning with Python. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5127-0.

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24

Sanner, Scott, and Marcus Hutter, eds. Recent Advances in Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29946-9.

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25

Whiteson, Shimon. Adaptive Representations for Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13932-1.

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26

Kaelbling, Leslie Pack, ed. Recent Advances in Reinforcement Learning. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/b102434.

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27

Majumder, Abhilash. Deep Reinforcement Learning in Unity. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6503-1.

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28

Girgin, Sertan, Manuel Loth, Rémi Munos, Philippe Preux, and Daniil Ryabko, eds. Recent Advances in Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89722-4.

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29

Kaelbling, Leslie Pack. Recent advances in reinforcement learning. Boston: Kluwer Academic, 1996.

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30

Williams, Ronald. Reinforcement learning: Technical tutorial seminar. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 1989.

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31

Whiteson, Shimon. Adaptive representations for reinforcement learning. Berlin: Springer Verlag, 2010.

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32

Sanghi, Nimish. Deep Reinforcement Learning with Python. Berkeley, CA: Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0273-7.

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33

Xiao, Liang, Helin Yang, Weihua Zhuang, and Minghui Min. Reinforcement Learning for Maritime Communications. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32138-2.

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34

Hu, Michael. The Art of Reinforcement Learning. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9606-6.

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35

Bhandari, Jalaj. Optimization Foundations of Reinforcement Learning. [New York, N.Y.?]: [publisher not identified], 2020.

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36

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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37

Sutton, Richard S. Reinforcement Learning. Island Press, 1992.

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38

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

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39

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

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40

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

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41

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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42

Statistical Reinforcement Learning. CRC Press, 2012.

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43

Deep Reinforcement Learning. Springer Singapore Pte. Limited, 2022.

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44

Sugiyama, Masashi. Statistical Reinforcement Learning. Taylor & Francis Group, 2015.

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45

Bellemare, Marc G., Will Dabney, and Mark Rowland. Distributional Reinforcement Learning. MIT Press, 2023.

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46

Deep Reinforcement Learning. Springer Nature, 2019.

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47

Deep Reinforcement Learning. Springer Nature, 2020.

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48

Reinforcement Learning Adaptation Learning and Optimization. Springer, 2012.

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49

Mellouk, Abdelhamid, ed. Advances in Reinforcement Learning. InTech, 2011. http://dx.doi.org/10.5772/557.

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50

Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.

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