The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
Optimal and Autonomous Control Using Reinforcement Learning: A Survey
Abstract
This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.
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Cites
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Artificial intelligence for the metaverse: A survey
Huynh-The, T., Pham, Q.-V., Pham, X.-Q., Nguyen, T. T., Han, Z., & Kim, D.-S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105581 · Cited by 710
Liu, B., Wang, L., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2931179 · Cited by 242
Reinforcement learning for clinical decision support in critical care: comprehensive review
Liu, S., See, K. C., Ngiam, K. Y., Celi, L. A., Sun, X., & Feng, M. (2020) · Journal of Medical Internet Research · doi:10.2196/18477 · Cited by 225
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Metadata
- Authors
- Kiumarsi, B., Vamvoudakis, K. G., Modares, H., & Lewis, F. L.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2018-06
- Volume
- 29
- Issue
- 6
- Pages
- 2042-2062
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2772589676
- Cited by
- 891
- References
- 93
- FWCI
- 30.83
- Type
- journal article
- Language
- en
- Open access
- closed
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