The DOI matches its Crossref record; 174 of the 174 references it cites were found in a registry or an index.
Algorithms for Reinforcement Learning
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Citations
Cites
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
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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
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,680
Finite-time Analysis of the Multiarmed Bandit Problem
Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033
Watkins, C. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,297
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
(1999) · Cited by 5,147
Cited by
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471
“Uncertainty is important in reinforcement learning (RL) as well (Szepesvári, 2010).”
BackgroundMulti-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control
Chu, T., Wang, J., Codeca, L., & Li, Z. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2901791 · Cited by 1,046
Deep Reinforcement Learning: A Survey
Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., & Miao, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3207346 · Cited by 972
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Holzinger, A. (2016) · Brain Informatics · doi:10.1007/s40708-016-0042-6 · Cited by 906
Deep Reinforcement Learning: An Overview
Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762
Metadata
- Authors
- Szepesvári, C.
- Venue
- Synthesis Lectures on Artificial Intelligence and Machine Learning
- Publisher
- Springer International Publishing
- Published
- 2010
- Volume
- 4
- Issue
- 1
- Pages
- 1-103
- ISSN
- 1939-4608 (ISSN-L)
- 1939-4616
- OpenAlex
- W2073384958
- Cited by
- 1,049
- References
- 174
- FWCI
- 20.97
- Type
- book
- Language
- en
- Open access
- closed
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- Szepesvári, C. (2010). Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. https://doi.org/10.1007/978-3-031-01551-9
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.
