Stochastic Genetic Algorithm-Assisted Fuzzy Q-Learning for Robotic Manipulators
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-021-05379-z.pdf
Reference15 articles.
1. Gao, H., et al.: Neural network control of a two-link flexible robotic manipulator using assumed mode method. IEEE Trans. Ind. Inf. 15(2), 755–765 (2018)
2. Jin, L.; et al.: Robot manipulator control using neural networks: a survey. Neurocomputing 285, 23–34 (2018)
3. Radac, M.B.; et al.: Model-free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning. Int. J. Syst. Sci. 48(5), 1071–1083 (2017)
4. Luo, B.; et al.: Model-free optimal tracking control via critic-only Q-learning. IEEE Trans. Neural Netw. Learn. Syst. 27(10), 2134–2144 (2016)
5. Kumar, A.; Sharma, R.: Fuzzy lyapunov reinforcement learning for nonlinear systems. ISA Trans. 67, 151–159 (2017)
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