{"id":"W4402742288","doi":"10.1109/icjece.2024.3451965","title":"Mixed-Reward Multiagent Proximal Policy Optimization Method for Two-on-Two Beyond-Visual-Range Air Combat","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Guidance and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Aeronautical Science Foundation of China; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Range (aeronautics); Computer science; Artificial intelligence; Mathematical optimization; Mathematics; Engineering; Aerospace engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001233876,0.001141482,0.001435797,0.0004955265,0.000583732,0.0008679078,0.001159329,0.00137964,0.003198773],"category_scores_gemma":[0.002104126,0.0006129696,0.0007127818,0.000399388,0.0008088471,0.0006232436,0.00158085,0.001575456,0.0004333689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007910663,"about_ca_system_score_gemma":0.001731474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007044711,"about_ca_topic_score_gemma":0.004202424,"domain_scores_codex":[0.9995114,0.0001922686,0.00001996073,0.0000875749,0.0001039801,0.0000848012],"domain_scores_gemma":[0.99899,0.0006421653,0.00009445193,0.00003685804,0.0001405914,0.00009584441],"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":[0.00004851746,0.00002984704,0.0001771484,0.00004482016,0.00002287989,0.00005301259,0.0000362855,0.982453,0.0004953298,0.005261415,0.0004100849,0.01096759],"study_design_scores_gemma":[0.000006451913,0.00002153301,0.00002205354,0.000002554887,0.000003539533,0.000004911985,0.000003308096,0.998779,0.00008645681,0.0008761566,0.0001916912,0.000002294757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006952695,0.0002312251,0.9898506,0.0001300055,0.00004057838,0.00004935741,0.00001534423,0.0001907876,0.002539459],"genre_scores_gemma":[0.7416585,0.0003862966,0.2491037,0.0002055748,0.00006621354,0.0004451533,0.00009705436,0.0001057098,0.007931816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007044711,"threshold_uncertainty_score":0.01400739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00746812929050423,"score_gpt":0.2303256827995632,"score_spread":0.2228575535090589,"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."}}