{"id":"W4285202992","doi":"10.1109/tnse.2022.3171600","title":"Trajectory Design and Resource Allocation for Multi-UAV Networks: Deep Reinforcement Learning Approaches","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Government of Jiangsu Province; China Institute of Communications","keywords":"Reinforcement learning; Computer science; Resource allocation; Flexibility (engineering); Trajectory; Distributed computing; Base station; A priori and a posteriori; Resource management (computing); Artificial intelligence; Curse of dimensionality; Resource (disambiguation); Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001021427,0.0007647087,0.0009281868,0.0003597891,0.0003674013,0.0006515265,0.0008851848,0.001048099,0.001503886],"category_scores_gemma":[0.002244006,0.0004214533,0.0003810917,0.0003388908,0.0007663859,0.0007643324,0.0009217779,0.001288606,0.0001874856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001314106,"about_ca_system_score_gemma":0.001329735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009817008,"about_ca_topic_score_gemma":0.00677215,"domain_scores_codex":[0.9996673,0.0001192556,0.00001494157,0.00006356325,0.00005893937,0.00007600933],"domain_scores_gemma":[0.9989416,0.0006387621,0.0001415483,0.0000447375,0.0001548425,0.00007854956],"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":[0.00001720032,0.00001598387,0.0002181776,0.00001258762,0.00000816341,0.00002004635,0.000017321,0.9887168,0.0002746477,0.002817012,0.0002157262,0.007666285],"study_design_scores_gemma":[0.000001782178,0.000004892647,0.00001309812,0.000001204857,8.407278e-7,0.000001533868,0.000001862538,0.9991635,0.00003729871,0.0007182911,0.00005504565,7.167681e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02233719,0.0003898871,0.9742606,0.0003449566,0.00003436323,0.00003202791,0.00002471844,0.0001353607,0.002440946],"genre_scores_gemma":[0.930503,0.0002283112,0.06633029,0.0001569788,0.00003136187,0.00009719925,0.00005674222,0.00004095057,0.002555193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009817008,"threshold_uncertainty_score":0.01951975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02794606252050497,"score_gpt":0.2014350778279712,"score_spread":0.1734890153074662,"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."}}