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Considering the stochastic dynamic characteristics of multi\u2010UAVs\u2010assisted MEC systems and the precision of spectrum resources, the deep reinforcement learning (DRL) algorithm and the non\u2010orthogonal multiple access (NOMA) techniques are introduced. Specifically, we design an offloading algorithm based on a multi\u2010agent deep deterministic policy gradient that jointly optimizes the UAVs' flight trajectories, the sensors' offloading powers, and the dynamic spectrum access to maximize the number of successfully offloaded tasks. The algorithm employs the Gumbel\u2010Softmax method to effectively control both the discrete sensor access action and the continuous offloading power action. Sufficient simulation results show that the proposed algorithm performs significantly better than other benchmark\u00a0algorithms.<\/jats:p>","DOI":"10.1049\/cmu2.70063","type":"journal-article","created":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T03:51:43Z","timestamp":1755921103000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Reinforcement Learning\u2010Based Intelligent Resource Management in Multi\u2010UAVs\u2010Assisted MEC Emergency Communication System"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6599-1261","authenticated-orcid":false,"given":"Yuanmo","family":"Lin","sequence":"first","affiliation":[{"name":"College of Communications Engineering Army Engineering University of PLA Nanjing China"},{"name":"Department of Electromechanical and Information Engineering College of Putian University Putian 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