Page 141 - Read Online
P. 141

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.
   136   137   138   139   140   141   142   143   144   145   146