{"id":"W4309433924","doi":"10.3390/rs14225662","title":"UAV-Assisted Fair Communication for Mobile Networks: A Multi-Agent Deep Reinforcement Learning Approach","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Throughput; Weighting; Base station; Convergence (economics); Real-time computing; Distributed computing; Computer network; Wireless; Artificial intelligence; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001028351,0.0007545276,0.0008444907,0.000330926,0.0004815157,0.0007250842,0.001075472,0.0008593947,0.001061579],"category_scores_gemma":[0.002309826,0.0002977824,0.0003767557,0.0002725644,0.0008157856,0.0009098073,0.0009381808,0.001129868,0.0001028805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001234834,"about_ca_system_score_gemma":0.001354556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01132366,"about_ca_topic_score_gemma":0.007918132,"domain_scores_codex":[0.9996282,0.0001111313,0.00001590226,0.00008457454,0.00007305558,0.00008723144],"domain_scores_gemma":[0.9990693,0.0005445363,0.0001161621,0.00004150307,0.0001610261,0.0000675487],"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":[0.00003210207,0.00002793325,0.0005644694,0.00002091784,0.00001846592,0.00003851958,0.00003352993,0.978519,0.0006491331,0.004537838,0.0003044433,0.01525366],"study_design_scores_gemma":[0.000002430218,0.000007525985,0.00002320451,0.000001224508,0.000002004793,0.000002450244,0.000002645616,0.9989905,0.00007062882,0.0008438307,0.00005259732,0.000001095168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04691562,0.0004540711,0.9488508,0.000400843,0.00005746552,0.00003898251,0.0000214423,0.0002669816,0.002993746],"genre_scores_gemma":[0.9642307,0.0001418911,0.03381419,0.0001318445,0.00002447998,0.00005505275,0.0000264399,0.0000223408,0.001553168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01132366,"threshold_uncertainty_score":0.02251548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01756464235694958,"score_gpt":0.2309569973787467,"score_spread":0.2133923550217971,"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."}}