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Communications system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel.
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U. Aßmann, C. Baier, C. Dubslaff, D. Grzelak, S. Hanisch, A. P. P. Hartono, S. Köpsell, T. Lin, and T. Strufe, “Tactile computing: Essential building blocks for the tactile internet,” in Tactile Internet . Elsevier, 2021, pp. 293–317
2021
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W. Wang, Y. Liu, R. Srikant, and L. Ying, “3M-RL: Multi-resolution, multi-agent, mean-field reinforcement learning for autonomous UAV routing,” IEEE Transactions on Intelligent Transportation Systems , pp. 1–12, 2021
2021
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T. Ren, J. Niu, B. Dai, X. Liu, Z. Hu, M. Xu, and M. Guizani, “Enabling efficient scheduling in large-scale UAV-assisted mobile edge computing via hierarchical reinforcement learning,” IEEE Internet of Things Journal , pp. 1–1, 2021
2021
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H. Chang, Y. Chen, B. Zhang, and D. Doermann, “Multi-UAV mobile edge computing and path planning platform based on reinforcement learning,” IEEE Transactions on Emerging Topics in Computational Intelligence , pp. 1–10, 2021
2021
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L. Wang, K. Wang, C. Pan, W. Xu, N. Aslam, and A. Nallanathan, “Deep reinforcement learning based dynamic trajectory control for UAV-assisted mobile edge computing,” IEEE Transactions on Mobile Computing , pp. 1–1, 2021
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Y. Yuan, L. Lei, T. X. Vu, S. Chatzinotas, S. Sun, and B. Ottersten, “Energy minimization in UAV-aided networks: Actor-critic learning for constrained scheduling optimization,” IEEE Transactions on Vehicular Technology , pp. 1–1, 2021
2021
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J. Park, S. Samarakoon, A. Elgabli, J. Kim, M. Bennis, S. L. Kim, and M. Debbah, “Communication-efficient and distributed learning over wireless networks: Principles and applications,” Proc. of the IEEE , vol. 109, no. 5, pp. 796–819, May 2021
2021
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S. Arora and P. Doshi, “A survey of inverse reinforcement learning: Challenges, methods and progress,” Artificial Intelligence , vol. 297, 2021
2021
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T. Fernando, S. Denman, S. Sridharan, and C. Fookes, “Deep inverse reinforcement learning for behavior prediction in autonomous driving: Accurate forecasts of vehicle motion,” IEEE Signal Processing Magazine , vol. 38, no. 1, pp. 87–96, 2021
2021
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X. Luo, H.-H. Chen, and Q. Guo, “Semantic communications: Overview, open issues, and future research directions,” IEEE Wire. Commun. , vol. 29, no. 1, pp. 210–219, 2022
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Y. Zhen, W. Chen, L. Zheng, X. Li, and D. Mu, “Multi-agent cooperative caching policy in industrial internet of things,” IEEE Internet of Things J. , pp. 1–1, 2022
2022
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N. H. Chu, D. T. Hoang, D. N. Nguyen, N. Van Huynh, and E. Dutkiewicz, “Joint speed control and energy replenishment optimization for UAV-assisted IoT data collection with deep reinforcement transfer learning,” IEEE Internet of Things J. , pp. 1–1, 2022
2022
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Z. Chang, H. Deng, L. You, G. Min, S. Garg, and G. Kaddoum, “Trajectory design and resource allocation for multi-UAV networks: Deep reinforcement learning approaches,” IEEE Transactions on Network Science and Engineering , pp. 1–1, 2022
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R. Zhang, Q. Zong, X. Zhang, L. Dou, and B. Tian, “Game of drones: Multi-uav pursuit-evasion game with online motion planning by deep reinforcement learning,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–10, 2022
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C. Zhan and Y. Zeng, “Energy minimization for cellular-connected UAV: From optimization to deep reinforcement learning,” IEEE Transactions on Wireless Communications , pp. 1–1, 2022
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V.-D. Nguyen, S. Chatzinotas, B. Ottersten, and T. Q. Duong, “FedFog: Network-aware optimization of federated learning over wireless fog-cloud systems,” IEEE Trans. Wire. Commun. , pp. 1–18, 2022
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