The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Reinforcement Learning and Markov Decision Processes
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Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Artificial intelligence: a modern approach
(1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 21,974
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,583
(1995) · Cited by 10,558
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Puterman, M. L. (1994) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316887 · Cited by 6,024
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Digital twin paradigm: A systematic literature review
Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103469 · Cited by 986
Deep learning enabled inverse design in nanophotonics
So, S., Badloe, T., Noh, J., Rho, J., & Bravo‐Abad, J. (2020) · Nanophotonics · doi:10.1515/nanoph-2019-0474 · Cited by 564
Machine learning in medical applications: a review of state-of-the-art methods
Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105458 · Cited by 542
A Survey of Multi-Objective Sequential Decision-Making
Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3987 · Cited by 461
DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning
Gao, G., Li, J., & Wen, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2992117 · Cited by 283
Metadata
- Authors
- van Otterlo, M., & Wiering, M.
- Venue
- Adaptation, Learning, and Optimization
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 3-42
- ISSN
- 1867-4534 (ISSN-L)
- 1867-4542
- OpenAlex
- W169931978
- Cited by
- 628
- References
- 67
- FWCI
- 6.91
- Type
- book chapter
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- CoChat. (2026). Citation check: Reinforcement Learning and Markov Decision Processes. https://app.cochat.ai/papers/10.1007/978-3-642-27645-3_1
- The paper
- van Otterlo, M., & Wiering, M. (2012). Reinforcement Learning and Markov Decision Processes. Adaptation, Learning, and Optimization. https://doi.org/10.1007/978-3-642-27645-3_1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
