{"id":"W4392543822","doi":"10.1109/access.2024.3374384","title":"Reinforcement Learning Placement Algorithm for Optimization of UAV Network in Wireless Communication","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Reinforcement learning; Wireless; Wireless network; Optimization algorithm; Algorithm; Computer network; Artificial intelligence; Mathematical optimization; Telecommunications; 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":[0.0009882451,0.001283698,0.001571619,0.0006821306,0.0005032013,0.0007623647,0.0009758213,0.001248254,0.003314566],"category_scores_gemma":[0.002330255,0.0005014404,0.0006280004,0.0006328654,0.0008346086,0.0006316472,0.001026326,0.001232879,0.0005010656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001020721,"about_ca_system_score_gemma":0.001307866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008233882,"about_ca_topic_score_gemma":0.005445278,"domain_scores_codex":[0.99958,0.0001602577,0.00001783219,0.00008350368,0.00008785663,0.00007057001],"domain_scores_gemma":[0.9990038,0.0006410123,0.0001178863,0.00002369563,0.0001646154,0.00004901847],"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":[0.00002705325,0.00002530595,0.0002568974,0.00004011113,0.00001784259,0.00004867714,0.00002926585,0.9826821,0.0004210234,0.002558192,0.0005298682,0.01336377],"study_design_scores_gemma":[0.000007452035,0.0000230368,0.00003137834,0.000004078447,0.000002731318,0.000007028006,0.000005815652,0.9988122,0.00008459735,0.0008407757,0.0001786233,0.000002244059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009412723,0.0003542405,0.9868591,0.0001468242,0.00005267696,0.00006681542,0.00002612737,0.0001830538,0.002898528],"genre_scores_gemma":[0.7439938,0.0006428583,0.2477951,0.0002366221,0.00008294887,0.0005717471,0.0001772057,0.0001110685,0.006388635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008233882,"threshold_uncertainty_score":0.01637191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456592446331313,"score_gpt":0.2719590892131106,"score_spread":0.2573931647497975,"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."}}