{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002389941,0.0001174286,0.0001257286,0.00007166126,0.0005567104,0.00004291676,0.0001061664,0.00004303976,0.00001032131],"category_scores_gemma":[0.00001046786,0.0001417925,0.00006146774,0.000262797,0.00001352545,0.00005528396,0.00008112402,0.0002170817,0.000002739734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002614383,"about_ca_system_score_gemma":0.00000878887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002291209,"about_ca_topic_score_gemma":0.000007785688,"domain_scores_codex":[0.9992244,0.00005359646,0.0002435146,0.0001581192,0.0001138091,0.0002065574],"domain_scores_gemma":[0.9994684,0.00004681148,0.0000715218,0.0003208345,0.0000521017,0.00004030063],"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":[0.000004964778,0.00001043453,0.000001644091,0.00002075733,0.00002023796,1.818115e-7,0.000443214,0.913422,0.0004533147,0.0000237555,0.00008260727,0.08551683],"study_design_scores_gemma":[0.0003561527,0.00002398635,0.00001989446,0.00001034121,0.00002246848,0.00001017569,0.0006763495,0.9865294,0.00006135775,0.000008401684,0.01212797,0.0001535795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005578832,0.0004741728,0.9916453,0.00001595018,0.00007572806,0.0007268091,7.643357e-7,0.0003342779,0.001148134],"genre_scores_gemma":[0.7779973,0.0001051734,0.2212234,0.00002918928,0.0000336346,0.000006561765,0.0003466671,0.00004426705,0.0002138191],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7724184,"threshold_uncertainty_score":0.5782131,"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."}}