• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
D. Silver, et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484-489, 2016. DOI:10.1038/nature16961DOI
2 
L. P. Kaelbling, M. L. Littman, A. W. Moore, “Reinforcement learning: A survey,” Journal of artificial intelligence research, vol. 4, pp. 237-285, 1996. DOI:10.1613/jair.301DOI
3 
B. Osiński et al., “Simulation-Based Reinforcement Learning for Real-World Autonomous Driving,” 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, pp. 6411-6418, 2020. DOI:10.1109/ICRA40945.2020.9196730DOI
4 
T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science robotics, vol. 7, no. 62, eabk2822, 2022. DOI:10.1126/scirobotics.abk2822DOI
5 
S. A, A. N. Rafferty, et al., “Reinforcement learning for education: Opportunities and challenges,” arXiv preprint arXiv:2017.08828, 2021. DOI:10.48550/arXiv.2107.08828DOI
6 
B. Fahad Mon, A. Wasfi, et al., “Reinforcement Learning in Education: A Literature Review,” Informatics, vol. 10, no. 3, pp. 74-95, 2023. DOI:10.3390/informatics10030074DOI
7 
H. Gharbi, L. Elaachak, A. Fennan, “Reinforcement Learning Algorithms and Their Applications in Education Field: A Systematic Review,” The Proceedings of the International Conference on Smart City Applications, pp. 410-418, 2023. DOI:10.1007/978-3-031-54376-0_37DOI
8 
H. Shin, and H. Oh, “Neural Network Model Compression Algorithms for Image Classification in Embedded Systems,” The Journal of Korea Robotics Society, vol. 17, no. 2, pp. 133-141, 2022. DOI:10.7746/jkros.2022.17.2.133DOI
9 
W. Zhao, J. P. Queralta, and T. Westerlund, “Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey,” 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 737-744, 2020. DOI:10.1109/SSCI47803.2020.9308468DOI
10 
M. A. Perez-Cisneros, R. Leal-Ascencio, and P. A. Cook, “Reinforcement learning neurocontroller applied to a 2-DOF manipulator,” Proceeding of the 2001 IEEE International Symposium on Intelligent Control (ISIC’01), pp. 56-61, 2001. DOI:10.1109/ISIC.2001.971484DOI
11 
Y. Cheng, P. Zhao, F. Wang, D. J. Block, and Hovakimyan, “Improving the Robustness of Reinforcement Learning Policies With L1 Adaptive Control,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6574-6581, 2022. DOI:10.1109/LRA.2022.3169309DOI
12 
T. Lee, D. Ju, and Y. S. Lee, “Development Environment of Reinforcement Learning-based Controllers for Real-world Physical Systems Using LW-RCP,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 29, no. 7, pp. 543-549, 2023. DOI:10.5302/J.ICROS.2023.23.0045DOI
13 
T. Lee, D. Ju, and Y. S. Lee, “Sim-to-Real Reinforcement Learning Techniques for Double Inverted Pendulum Control with Recovery Property,” The transactions of The Korean Institute of Electrical Engineers (in Korean), vol. 72, no. 12, pp. 1705-1713, 2023. DOI:10.5370/KIEE.2023.72.12.1705DOI
14 
G. Dulac-Arnold, N. Levine, D. J. Mankowits, J. Li, C. Paduraru, S. Gowal, and T. Hester, “Challenges of real-world reinforcement learning: definitions, benchmarks and analysis,” Machine Learning, vol. 110, no. 9, pp. 2419-2648, 2021. DOI:10.1007/s10994-021-05961-4DOI
15 
A. Paszke, S. Gross, F. Massa, and et al., “Pytorch: An impressive style, high-performance deep learning library,” Advances in Neural Information Processing Systems, vol. 32, 2019.URL
16 
M. Abadi, P. Barham, J. Chen, and et al., “Tensorflow: A system for large-scale machine learning,” Osdi, vol. 16, no. 2016, pp 265-283, 2016.URL
17 
Y. S. Lee, D. Ju, C. Choi, “Development of Educational Environment to Improve Efficiency of Online Education on Control Systems,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 27, no. 12, pp. 1056-1063, 2021. DOI:10.5302/J.ICROS.2021.21.0199DOI
18 
Y. Fujiyama, D. Ju and Y. S. Lee, “The Implementation of a Ball and Plate System using a 3-DOF Stewart Platform and LW-RCP,” The transactions of The Korean Institute of Electrical Engineers (in Korean), vol. 72, no. 8, pp. 943-951, 2023. DOI:10.5370/KIEE.2023.72.8.943DOI
19 
A. Kuznetsov, P. Shvechikov, A. Grishin and D. Vetrov, “Controlling overestimation bias with truncated mixture of continuous distributional quantile critics,” in International Conference on Machine Learning, PMLR, pp. 5556-5566, 2020.URL
20 
W. Dabney, M. Rowland, M. Bellemare, and Munos R, “Distributional reinforcement learning with quantile regression,” in Proceedings of the AAAI conference on artificial intelligence, vol. 32, no. 1, pp. 2892-2901, 2018. DOI:10.1609/aaai.v32i1.11791DOI
21 
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” International conference on machine learning. PMLR, pp. 1861-1870, 2018.URL