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However, it is difficult to guarantee the freshness of information delivery for the energy-limited UAV. Thus, we study the trajectory design in the multiple-UAV communication system, in which the massive ground devices send the individual information to mobile UAV base stations under the demand of information freshness. First, an energy-efficiency (EE) maximization optimization problem is formulated under the rest energy, safety distance, and age of information (AoI) constraints. However, it is difficult to solve the optimization problem due to the nonconvex objective function and unknown dynamic environment. Second, a trajectory design based on the deep Q-network method is proposed, in which the state space considering energy efficiency, rest energy, and AoI and the efficient reward function related with EE performance are constructed, respectively. Furthermore, to avoid the dependency of training data for the neural network, the experience replay and random sampling for batch are adopted. Finally, we validate the system performance of the proposed scheme. Simulation results show that the proposed scheme can achieve a better EE performance compared with the benchmark scheme.<\/jats:p>","DOI":"10.1155\/2021\/1430512","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T17:23:34Z","timestamp":1640280214000},"page":"1-9","source":"Crossref","is-referenced-by-count":26,"title":["Energy-Efficient UAV Trajectory Design with Information Freshness Constraint via Deep Reinforcement Learning"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6274-9576","authenticated-orcid":true,"given":"Xinmin","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China"}]},{"given":"Jiahui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6213-1880","authenticated-orcid":true,"given":"Dandan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Weapon Engineering, Naval University of Engineering, Wuhan 430030, China"}]}],"member":"311","reference":[{"article-title":"Cisco visual networking index: Global Mobile Data traffic forecast update 2017\u20132022","year":"2019","author":"Cisco","key":"1"},{"volume-title":"Key Drivers and Research Challenges for 6G Ubiquitous Wireless Intelligence","year":"2019","author":"G. 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