{"id":"W3084206880","doi":"10.48550/arxiv.2009.03721","title":"DDPG-based Resource Management for MEC/UAV-Assisted Vehicular Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Resource allocation; Resource management (computing); Scheme (mathematics); Controller (irrigation); Quality of service; Resource (disambiguation); Enhanced Data Rates for GSM Evolution; Distributed computing; Mathematical optimization; Computer network; Artificial intelligence; Mathematics","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":[0.0005970963,0.0005232732,0.0005567314,0.0002505704,0.0003099104,0.0004978269,0.0008568864,0.0004972163,0.0007468765],"category_scores_gemma":[0.001502591,0.0002374693,0.0001859621,0.0002504519,0.0006230117,0.0007119174,0.0008700573,0.0005929334,0.00009630516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009064608,"about_ca_system_score_gemma":0.0009661696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006416574,"about_ca_topic_score_gemma":0.005145025,"domain_scores_codex":[0.9996676,0.0001088861,0.00001100519,0.00006697077,0.00005915202,0.000086451],"domain_scores_gemma":[0.9995391,0.0002325565,0.00006948516,0.00003468211,0.00007658207,0.00004762532],"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":[0.00002157988,0.00001815774,0.0003090832,0.00001335521,0.000008224707,0.00002847488,0.0000133573,0.9879826,0.0007422971,0.002644716,0.0001857108,0.008032466],"study_design_scores_gemma":[0.000001336947,0.000008894004,0.00003157727,8.250431e-7,0.000001032226,0.000004189915,0.000003556769,0.9991016,0.0001471647,0.0006162889,0.00008267461,8.193693e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07949027,0.0005351315,0.9158446,0.0003284916,0.00006357302,0.00005083131,0.00003879594,0.0001844317,0.003463992],"genre_scores_gemma":[0.9769274,0.00009883825,0.0219317,0.00004637875,0.00001082162,0.00002440816,0.0000231165,0.00001218098,0.0009250967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006416574,"threshold_uncertainty_score":0.01275843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04248774155388247,"score_gpt":0.1594502898138618,"score_spread":0.1169625482599793,"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."}}