{"id":"W3135046321","doi":"10.1109/gcwkshps50303.2020.9367580","title":"Mobile cellular-connected UAVs: reinforcement learning for sky limits","year":2020,"lang":"en","type":"article","venue":"Spiral (Imperial College London)","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Handover; Key (lock); Task (project management); Energy consumption; Range (aeronautics); Function (biology); Mobile robot; Artificial intelligence; Real-time computing; Cellular network; Mathematical optimization; Robot; Computer network; Engineering; Mathematics; Aerospace engineering; Computer security","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":[0.00007199767,0.0002107737,0.0002422359,0.00005257334,0.0001826854,0.00005817126,0.0001694653,0.0001225119,0.0002527315],"category_scores_gemma":[0.00005749751,0.0002268541,0.0001051245,0.0003694642,0.00002129601,0.0001505862,0.00003288406,0.0001478889,0.0001009886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007621521,"about_ca_system_score_gemma":0.00003598922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006913289,"about_ca_topic_score_gemma":0.000005197205,"domain_scores_codex":[0.9989153,0.00001646873,0.000348101,0.0002586413,0.0001389943,0.0003224812],"domain_scores_gemma":[0.9995062,0.00003525076,0.0000540507,0.0001670475,0.00008881088,0.0001485759],"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":[0.0001970405,0.00002521722,0.00004777204,0.0001539905,0.00006344179,0.000003826718,0.0007148793,0.8676702,0.1167965,0.007700382,0.004758972,0.001867691],"study_design_scores_gemma":[0.002356983,0.0008191888,0.00001292295,0.00001623486,0.00004692076,0.000001299411,0.0002226953,0.7132533,0.07363863,0.000100331,0.209068,0.0004634976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6969401,0.0009738255,0.2719837,0.0004305646,0.001557585,0.005761192,0.0001384651,0.002419585,0.01979497],"genre_scores_gemma":[0.9940773,0.00006323855,0.003461699,0.0001852138,0.0007215181,0.0006744734,0.0002266898,0.00007020408,0.0005197229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2971372,"threshold_uncertainty_score":0.9250842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01210657529506352,"score_gpt":0.2106602494189186,"score_spread":0.1985536741238551,"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."}}