Page 68 - Read Online
P. 68
Page 166 Boin et al. Intell Robot 2022;2(2):14567 I http://dx.doi.org/10.20517/ir.2022.11
REFERENCES
1. McMahan HB, Moore E, Ramage D, Arcas BA. Federated learning of deep networks using model averaging. ArXiv, vol. abs/1602.05629,
2016.
2. Konecný J, McMahan HB, Ramage D. Federated optimization: Distributed optimization beyond the datacenter. ArXiv, vol.
abs/1511.03575, 2015.
3. McMahan HB, Moore E, Ramage D, Hampson S, Arcas BA. Communicationefficient learning of deep networks from decentralized data.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, vol. 54, 2017.
4. Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: concept and Applications. arXiv, vol. 10, no. 2, pp. 1–19, 2019.
5. Lim HK, Kim JB, Heo JS, Han YH. Federated reinforcement learning for training control policies on multiple iot devices. Sensors
(Basel) 2020;20:1359. Available: https://doi.org/10.3390/s20051359
6. Ye Y, Zhang X, Sun J. Automated vehicle’s behavior decision making using deep reinforcement learning and highfidelity simulation
environment. Transportation Research Part C: Emerging Technologies 2019;107:15570. Available: https://doi.org/10.1016/j.trc.2019.0
8.011
7. Zhu M, Wang X, Wang Y. Humanlike autonomous carfollowing model with deep reinforcement learning. Transportation Research
Part C: Emerging Technologies 2018;97:34868. Available: https://doi.org/10.1016/j.trc.2018.10.024
8. Song X, Chen L, Wang K, He D. Robust timedelay feedback control of vehicular cacc systems with uncertain dynamics. Sensors (Basel)
2020;20:1775. Available: https://doi.org/10.3390/s20061775
9. Chu T, Kalabic U. Modelbased deep reinforcement learning for CACC in mixedautonomy vehicle platoon. Proceedings of the IEEE
Conference on Decision and Control. vol. 2019December, pp. 407984. [Online]. Available: https://doi.org/10.1109/CDC40024.2019.
9030110
10. Nadiger C, Kumar A, Abdelhak S. Federated reinforcement learning for fast personalization. 2019 IEEE Second International Conference
on Artificial Intelligence and Knowledge Engineering (AIKE), 2019, pp. 123127. Available: https://doi.org/10.1109/AIKE.2019.00031
11. Qi J, Zhou Q, Lei L, Zheng K. Federated reinforcement learning: Techniques, applications, and open challenges. Intell Robot2021;1:1857.
https://doi.org/10.20517/ir.2021.02
12. Sutton RS, Barto AG. Reinforcement learning: An introduction. MIT press, 2018.
13. Yang Q, Liu Y, Cheng Y, Kang Y, Chen T, Yu H. Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning
2019;13:1207. Available: https://doi.org/10.2200/S00960ED2V01Y201910AIM043
14. Liu B, Wang L, Liu M. Lifelong federated reinforcement learning: a learning architecture for navigation in cloud robotic systems. arXiv,
vol. 4, no. 4, pp. 4555–4562, 2019.
15. Liang X, Liu Y, Chen T, Liu M, Yang Q. Federated transfer reinforcement learning for autonomous driving. arXiv, 2019.
16. Zhang X, Peng M, Yan S, Sun Y. Deepreinforcementlearningbased mode selection and resource allocation for cellular v2x communi
cations. IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6380–6391, 2020.
17. Lim H, Kim J, Ullah I, Heo J, Han Y. Federated reinforcement learning acceleration method for precise control of multiple devices. IEEE
Access, vol. 9, pp. 76 296–76 306, 2021.
18. Wang X, Li R, Wang R, Li X, Taleb T, Leung VCM. Attentionweighted federated deep reinforcement learning for devicetodevice
assisted heterogeneous collaborative edge caching. IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 154–169,
2021.
19. Huang H, Zeng C, Zhao Y, Min G, Zhu Y, Miao W, Hu J. Scalable orchestration of service function chains in nfvenabled networks: A
federated reinforcement learning approach. IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2558–2571, 2021.
20. Makantasis K, Kontorinaki M, Nikolos I. Deep reinforcementlearningbased driving policy for autonomous road vehicles. IET Intelligent
Transport Systems 2020;14:1324. [Online]. Available: https://doi.org/10.1049/ietits.2019.0249
21. Sallab AE, Abdou M, Perot E, Yogamani S. Deep reinforcement learning framework for autonomous driving. IS and T International
Symposium on Electronic Imaging Science and Technology 2017;29:706. [Online]. Available: https://doi.org/10.2352/ISSN.24701173
.2017.19.AVM023
22. Lin Y, McPhee J, Azad NL. Longitudinal dynamic versus kinematic models for carfollowing control using deep reinforcement learning.
2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019. pp. 1504–1510.
23. Peake A, McCalmon J, Raiford B, Liu T, Alqahtani S. Multiagent reinforcement learning for cooperative adaptive cruise control.
2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), 2020, pp. 1522 [Online]. Available:
https://doi.org/10.1109/ICTAI50040.2020.00013
24. Lillicrap TP, Hunt JJ, Pritzel A, et al. Continuous control with deep reinforcement learning. 4th International Conference on Learning
Representations, ICLR 2016 Conference Track Proceedings, 2016.