Continuous deep Q-learning with a simulator for stabilization of uncertain discrete-time systems

Author:

Ikemoto Junya1ORCID,Ushio Toshimitsu1ORCID

Affiliation:

1. Graduate School of Engineering Science, Osaka University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Rehabilitation,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine

Reference37 articles.

1. [1] H.K. Khalil, Nonlinear Systems, Prentice hall, 2002.

2. [2] R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction (second edition), MIT Press, 2018.

3. [3] C. Szepesvari, Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning), Morgan and Claypool Publishers, 2010.

4. [4] J. Kober, J.A. Bagnell, and J. Peters, “Reinforcement learning in robotics: A survey,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1238-1274, August 2013.

5. [5] T. Kamio, S. Sugeo, K. Mitsubori, T. Tanaka, C.J. Ahn, H. Fujisaka, and K. Haeiwa, “A reinforcement learning approach to course decision of ships under navigation rules,” Proc. NOLTA'09, pp. 141-144, 2009.

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