The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Linear Least-Squares algorithms for temporal difference learning
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Cites
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Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Adaptive filtering, prediction and control
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Theory and Practice of Recursive Identification
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Adaptive filtering prediction and control
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Neuronlike adaptive elements that can solve difficult learning control problems
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Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Algorithms for Reinforcement Learning
Szepesvári, C. (2010) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01551-9 · Cited by 1,049
A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients
Grondman, I., Busoniu, L., Lopes, G. A. D., & Babuska, R. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2218595 · Cited by 1,042
Deep Reinforcement Learning: An Overview
Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762
A survey on fake news and rumour detection techniques
Bondielli, A., & Marcelloni, F. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.035 · Cited by 658
Metadata
- Authors
- Bradtke, S. J., & Barto, A. G.
- Venue
- Machine Learning
- Published
- 1996
- Volume
- 22
- Issue
- 1-3
- Pages
- 33-57
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2072931156
- Cited by
- 599
- References
- 30
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Bradtke, S. J., & Barto, A. G. (1996). Linear Least-Squares algorithms for temporal difference learning. Machine Learning. https://doi.org/10.1007/bf00114723
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.
