## 4 Proposed Trajectory Optimization Approach
### Proposed H-DQN Algorithm
(2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Survey on Reinforcement Learning Applications in Communication Networks. _J. Commun. Inf. Netw._**2019**, \(4\), 30-39. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networks. In Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 9-13 December 2019; pp. 1-6. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep RL-based Trajectory Planning for AoI Minimization in UAV-assisted IoT. In Proceedings of the 2019 11 th International Conference on Wireless Communications and Signal Processing (WCSP), Xi'an, China, 23-25 October 2019; pp. 1-6. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] _Age of Information: A New Metric for Information: A New Metric for Information Freshness_; Morgan & Claypool: San Rafael, CA, USA, 2019. * [PERSON] et al. (2020) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Age of Information Aware Trajectory Planning of UAVs in Intelligent Transportation Systems: A Deep Learning Approach. _IEEE Trans. Veh. Technol._**2020**, _69_, 12382-12395. [CrossRef]* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Peak Age-of-Information Minimization of UAV-Aided Relay Transmission. In Proceedings of the 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 7-11 June 2020; pp. 1-6. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks. In Proceedings of the 2019 IEEE International Conference on Communications Workshops (ICC Workshops), Shanghai, China, 20-24 May 2019; pp. 716-721. * [PERSON] and Barto (1998) [PERSON]; [PERSON] _Reinforcement Learning_; A Bradford Book; MIT Press: Cambridge, MA, USA,1998; Volume 15, pp. 665-685. * [PERSON] (2019) [PERSON]; [PERSON] Modern Deep Reinforcement Learning Algorithms. _arXiv_**2019**, arXiv:1906.10025v1. * [PERSON] et al. (2013) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Playing Atari with Deep Reinforcement Learning. _arXiv_**2013**, arXiv:1312.5602v1. * [PERSON] et al. (2015) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; et al. Human-Level Control Through Deep Reinforcement Learning. _Nature_**2015**, _518_, 529-533. [CrossRef] [PubMedMed]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-UAV Dynamic Wireless Networking with Deep Reinforcement Learning. _IEEE Commun. Lett._**2019**, _23_, 2243-2246. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Reinforcement Learning for User Association in Heterogeneous Networks with Dual Connectivity. In Proceedings of the 2021 IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China, 29 March-1 April 2021; pp. 1-5. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Multi-UAV Aided Data Collection for Age Minimization in Wireless Sensor Networks. In Proceedings of the 2020 International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, 21-23 October 2020; pp. 80-85. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach. _IEEE Trans. Intell. Transp. Syst._**2021**, _22_, 5994-6006. [CrossRef]