{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:17:52Z","timestamp":1779362272417,"version":"3.51.4"},"reference-count":58,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    This work introduces a multiagent deep reinforcement learning (MADRL) framework for energy harvesting (EH) in unmanned aerial vehicle (UAV) networks aided by reconfigurable intelligent surfaces (RIS). The core goal is to maximize quantities of harvested energy subject to quality of service (QoS) constraints in dynamic wireless setups. The considered model involves centralized training alongside decentralized execution, together with replay\u2010based learning to yield stable convergence. Extensive experiments are included in a comparison between MADRL and evolution strategies (ES), deep deterministic policy gradient (DDPG), stochastic DDPG (SD3), and state of the art twin delayed DDPG (TD3)\u2013based approaches. The outcomes verify that MADRL ensures an average throughput of over 300\u2009Mbps when deployed with four UAVs, exceeding DDPG and closing in on TD3 and adaptive TD3, and consumes minimal processing time and memory resources. In time\u2010domain tests, MADRL maintains an EH fraction in the range of approximately 0.27\u20130.31 ((mean\u22480.29)), and in dual\u2010domain evaluation, it sustains an EH fraction of approximately 0.73\u20130.75 (mean\u22480.74), indicating robust energy performance under both scenarios. Parameter sensitivity analysis also confirms the selection of hyperparameter\n                    <jats:italic>\u03b1<\/jats:italic>\n                    ,\n                    <jats:italic>\u03b2<\/jats:italic>\n                    ,\n                    <jats:italic>\u03b7<\/jats:italic>\n                    , and\n                    <jats:italic>\u03b3<\/jats:italic>\n                    as optimal trade\u2010offs at\n                    <jats:italic>\u03b1<\/jats:italic>\n                    = 0.6,\n                    <jats:italic>\u03b2<\/jats:italic>\n                    = 0.8,\n                    <jats:italic>\u03b7<\/jats:italic>\n                    = 3 \u00d7 10\n                    <jats:sup>\u22124<\/jats:sup>\n                    ,\n                    <jats:italic>\u03b3<\/jats:italic>\n                    = 0.98. The computational tests verify potential practicability in real\u2010time applications, where MADRL only takes 0.42\u2009s and 1.8\u2009GBs, respectively, for each episode, and in terms of memory. These confirmations reflect the potential applicability of MADRL in scalable UAV RIS networks and thus provide potential applications in energy\u2010efficient wireless setups.\n                  <\/jats:p>","DOI":"10.1155\/int\/1477541","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:14:47Z","timestamp":1771485287000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multiagent Deep Reinforcement Learning Scheme for Energy Use Optimization in UAV\u2010Enabled Wireless Networks With Reconfigurable Intelligent Surfaces"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5888-4163","authenticated-orcid":false,"given":"Syed 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