The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Reinforcement learning and adaptive dynamic programming for feedback control
Abstract
Living organisms learn by acting on their environment, observing the resulting reward stimulus, and adjusting their actions accordingly to improve the reward. This action-based or reinforcement learning can capture notions of optimal behavior occurring in natural systems. We describe mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic programming. These give us insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior.
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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
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Optimal feedback control as a theory of motor coordination
Todorov, E., & Jordan, M. I. (2002) · Nature Neuroscience · doi:10.1038/nn963 · Cited by 3,081
Murray, J. J., Cox, C. J., Lendaris, G. G., & Saeks, R. (2002) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2002.801727 · Cited by 677
Al-Tamimi, A., Lewis, F. L., & Abu-Khalaf, M. (2007) · Automatica · doi:10.1016/j.automatica.2006.09.019 · Cited by 611
Self-citationHewer, G. (1971) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1971.1099755 · Cited by 326
Cited by
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
Lewis, F. L., & Vamvoudakis, K. G. (2011) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2043839 · Cited by 544
Self-citationVamvoudakis, K. G., & Lewis, F. L. (2011) · Automatica · doi:10.1016/j.automatica.2011.03.005 · Cited by 525
Self-citationModares, H., Lewis, F. L., & Naghibi-Sistani, M.-B. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2276571 · Cited by 462
Self-citationRobust Adaptive Dynamic Programming and Feedback Stabilization of Nonlinear Systems
Jiang, Y., & Jiang, Z.-P. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2294968 · Cited by 414
Metadata
- Authors
- Lewis, F. L., & Vrabie, D.
- Venue
- IEEE Circuits and Systems Magazine
- Published
- 2009
- Volume
- 9
- Issue
- 3
- Pages
- 32-50
- ISSN
- 0163-6812 (ISSN-L)
- 1531-636X
- 1558-0830
- OpenAlex
- W2165726932
- Cited by
- 1,558
- References
- 9
- FWCI
- 26.72
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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