The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming
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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
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Self-citationNeuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G., Sutton, R. S., & Anderson, C. W. (1983) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1983.6313077 · Cited by 3,233
Self-citationKorf, R. E. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90054-4 · Cited by 917
Pengi: an implementation of a theory of activity
Agre, P. E., & Chapman, D. (1987) · National Conference on Artificial Intelligence · Cited by 897
Dyna, an Integrated Architecture for Learning, Planning, and Reacting
Sutton, R. S. (1991) · ACM SIGART Bulletin · doi:10.1145/122344.122377 · Cited by 777 · cited 1×
“The policy is, in a sense, a plan, but one that is completely conditioned by current input.”
Temporal credit assignment in reinforcement learning
Sutton, R. S. (1984) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 749
Self-citationDynamic Programming and Markov Processes.
Kaufman, H., & Howard, R. A. (1961) · The American Mathematical Monthly · doi:10.2307/2312519 · Cited by 638
Schemata and Sequential Thought Processes in PDP Models
RUMELHART, D. E., SMOLENSKY, P., McCLELLAND, J. L., & HINTON, G. E. (1988) · Readings in Cognitive Science · doi:10.1016/b978-1-4832-1446-7.50020-0 · Cited by 626
Learning and Sequential Decision Making
Barto, A. G., Sutton, R. S., & Watkins, C. (1989) · Cited by 357
Self-citation
Cited by
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
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Deep reinforcement learning for autonomous driving: A survey
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Perez, P. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054625 · Cited by 2,342
Metadata
- Authors
- Sutton, R. S.
- Venue
- Machine Learning Proceedings 1990
- Publisher
- Elsevier
- Published
- 1990
- Pages
- 216-224
- OpenAlex
- W1491843047
- Cited by
- 1,314
- References
- 18
- FWCI
- 59.82
- 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: Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. https://app.cochat.ai/papers/10.1016/b978-1-55860-141-3.50030-4
- The paper
- Sutton, R. S. (1990). Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. Machine Learning Proceedings 1990. https://doi.org/10.1016/b978-1-55860-141-3.50030-4
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.
