The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Reinforcement Learning for Partially Observable Dynamic Processes: Adaptive Dynamic Programming Using Measured Output Data
Abstract
Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.
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“Optimal feedback control systems have been applied to basic sensorimotor control [97] and to reinforcement learning models [98].”
From Health behavior models in the age of mobile interventions: are our theories up to the task? (2011) · cites this paper as [98]
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
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. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,674
(2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5086 · Cited by 3,601
Lewis, F. L. (1986) · Cited by 2,957
Self-citationBeyond regression : new fools for prediction and analysis in the behavioral sciences
Werbos, P. J. (1974) · Medical Entomology and Zoology · Cited by 2,739
Approximate Dynamic Programming
Powell, W. B. (2011) · Wiley series in probability and statistics · doi:10.1002/9781118029176 · Cited by 2,359
Kronecker products and matrix calculus in system theory
Brewer, J. (1978) · IEEE Transactions on Circuits and Systems · doi:10.1109/tcs.1978.1084534 · Cited by 1,920
Online actor–critic algorithm to solve the continuous-time infinite horizon optimal control problem
Vamvoudakis, K. G., & Lewis, F. L. (2010) · Automatica · doi:10.1016/j.automatica.2010.02.018 · Cited by 1,722
Self-citationReinforcement learning and adaptive dynamic programming for feedback control
Lewis, F. L., & Vrabie, D. (2009) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2009.933854 · Cited by 1,591
Self-citation
Research works printed but not linked: 3
- sutton, 1998, Reinforcement LearningAn Introduction
- werbos, 1992, Handbook of Intelligent Control
- 1992, Handbook of Intelligent Control
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Lewis, F. L., Vrabie, D., & Vamvoudakis, K. G. (2012) · IEEE Control Systems · doi:10.1109/mcs.2012.2214134 · Cited by 1,122
Self-citationJiang, Y., & Jiang, Z. (2012) · Automatica · doi:10.1016/j.automatica.2012.06.096 · Cited by 1,079
Health behavior models in the age of mobile interventions: are our theories up to the task?
Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M., & Mermelstein, R. (2011) · Translational Behavioral Medicine · doi:10.1007/s13142-011-0021-7 · Cited by 978
Optimal and Autonomous Control Using Reinforcement Learning: A Survey
Kiumarsi, B., Vamvoudakis, K. G., Modares, H., & Lewis, F. L. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2773458 · Cited by 924
Self-citationZhang, H., Cui, L., Zhang, X., & Luo, Y. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2168538 · Cited by 645
Metadata
- Authors
- Lewis, F. L., & Vamvoudakis, K. G.
- Affiliations
- The University of Texas at Arlington (US)
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
- Published
- 2011-02
- Volume
- 41
- Issue
- 1
- Pages
- 14-25
- ISSN
- 1083-4419 (ISSN-L)
- 1941-0492
- OpenAlex
- W2151966330
- Cited by
- 544
- References
- 48
- Citations by year
- 2026: 22 · 2025: 49 · 2024: 46 · 2023: 43 · 2022: 37 · 2021: 49 · 2020: 53 · 2019: 34 · 2018: 52 · 2017: 26 · 2016: 35 · 2015: 19 · 2014: 33 · 2013: 18 · 2012: 19
- FWCI
- 15.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Dynamic Programming Control
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Keywords
- approximate dynamic programming, partially observable Markov decision processes, output feedback control, linear dynamical systems, policy iteration, value iteration, Q-learning, observability index
- MeSH terms
- Algorithms, Artificial Intelligence, Feedback, Learning, Markov Chains, Reinforcement, Psychology
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