The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Adaptive critic designs
Abstract
We discuss a variety of adaptive critic designs (ACDs) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins leads to an explanation of three design families: heuristic dynamic programming, dual heuristic programming, and globalized dual heuristic programming (GDHP). The main emphasis is on DHP and GDHP as advanced ACDs. We suggest two new modifications of the original GDHP design that are currently the only working implementations of GDHP. They promise to be useful for many engineering applications in the areas of optimization and optimal control. Based on one of these modifications, we present a unified approach to all ACDs. This leads to a generalized training procedure for ACDs.
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Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
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
(2013) · doi:10.4324/9780203763247 · Cited by 400
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
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Online learning control by association and reinforcement
Si, J., & Wang, Y.-T. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914523 · Cited by 773
Al-Tamimi, A., Lewis, F. L., & Abu-Khalaf, M. (2007) · Automatica · doi:10.1016/j.automatica.2006.09.019 · Cited by 611
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
(2012) · doi:10.1002/9781118453988 · Cited by 577
Self-citation
Metadata
- Authors
- Prokhorov, D. V., & Wunsch, D. C.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1997-09
- Volume
- 8
- Issue
- 5
- Pages
- 997-1007
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2136064843
- Cited by
- 1,186
- References
- 5
- FWCI
- 15.19
- 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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
