Adaptive exploration policy for exploration–exploitation tradeoff in continuous action control optimization-Reference-Cited by-同舟云学术

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Adaptive exploration policy for exploration–exploitation tradeoff in continuous action control optimization

Published:2021-08-10 Issue:12 Volume:12 Page:3491-3501
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Li Min,Huang Tianyi,Zhu William^ORCID

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-021-01387-5.pdf

Reference55 articles.

1. Yang ZY, Merrick K, Jin LW, Abbass HA (2018) Hierarchical deep reinforcement learning for continuous action control. IEEE Trans Neural Netw Learn Syst 29(11):5174–5184

2. Yang ZY, Merrick K, Abbass HA, Jin LW (2017) Multi-Task deep reinforcement learning for continuous action control. In: International joint conference on artificial intelligence, vol 17. Melbourne, Australia, pp 3301–3307. Accessed 19-25 Aug 2017

3. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

4. Huang TY, Wang SP, Zhu W (2020) An adaptive kernelized rank-order distance for clustering non-spherical data with high noise. Int J Mach Learn Cybern 11:1735–1747

5. The view from continuous control (2019) A tour of reinforcement learning: the view from continuous control. Annu Rev Control Robot Auton Syst 2:253–279

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