Architecture for Hierarchical System with Each Learning Structure
Author:
Affiliation:
1. Department of Mechanical and Intelligent Systems Engineering, Graduate School of Informatics and Engineering, The University of Electro-Communications
Publisher
The Institute of Systems, Control and Information Engineers
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://www.jstage.jst.go.jp/article/iscie/35/12/35_289/_pdf
Reference13 articles.
1. [1] R. S. Sutton and A. G. Barto: Reinforcement Learning, second edition: An Introduction, Bradford Books (2018)
2. [2] 森村: 強化学習, 講談社 (2019)
3. [3] G. Qu, A. Wierman and N. Li: Scalable reinforcement learning of localized policies for multi-agent networked systems; Proc. The 2nd Conference on Learning for Dynamics and Control(L4DC), pp. 256–266 (2020)
4. [4] G. Jing, H. Bai, J. George and A. Chakrabortty: Decomposability and parallel computation of multi-agent LQR; 2021 American Control Conference (ACC), pp. 4527–4532 (2021)
5. [5] G. Qu and N. Li: Exploiting fast decaying and locality in multi-agent MDP with tree dependence structure; Proc. 2019 IEEE 58th Conference on Decision and Control (CDC), pp. 6479–6486 (2019)
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