{"id":"W4417282961","doi":"10.1109/pimrc62392.2025.11274644","title":"Multi-Agent Deep Reinforcement Learning for Optimized Multi-UAV Coverage and Power-Efficient UE Connectivity","year":2025,"lang":"","type":"article","venue":"","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Science and Engineering Research Council","keywords":"Non-line-of-sight propagation; User equipment; Wireless; Wireless network; Reinforcement learning; Battlefield; Cellular network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000488637,0.0004549024,0.0004968758,0.000253609,0.0006018354,0.0002364224,0.0001664888,0.0002527469,0.0003231798],"category_scores_gemma":[0.0002241106,0.0004901179,0.0001702974,0.0004181721,0.00006674517,0.000136723,0.0001837492,0.0003030789,0.00002021937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003648527,"about_ca_system_score_gemma":0.00006836801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008882315,"about_ca_topic_score_gemma":0.00003153245,"domain_scores_codex":[0.9978286,0.00006006066,0.000717936,0.0006626738,0.0001689896,0.0005617377],"domain_scores_gemma":[0.9986818,0.0003444385,0.0001478061,0.0004229809,0.0002335939,0.0001694229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009132709,0.0002741334,0.0001553086,0.0002248668,0.000198892,6.984959e-7,0.0006511377,0.9889414,0.001370374,0.003298268,0.00008200145,0.004711606],"study_design_scores_gemma":[0.006928867,0.0001064552,0.000995074,0.00008500808,0.0001452157,0.000001215143,0.0004118887,0.9844717,0.001866542,0.000009483857,0.004497911,0.00048058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01188587,0.0009432778,0.9808045,0.0001726114,0.0004663518,0.002979457,0.000009245716,0.000253247,0.002485428],"genre_scores_gemma":[0.8457603,0.0006864698,0.1484573,0.0001094923,0.00002113755,0.0004984224,0.00005840901,0.00005092412,0.004357509],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8338745,"threshold_uncertainty_score":0.999755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01148418081596117,"score_gpt":0.2494582367531668,"score_spread":0.2379740559372056,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}