{"id":"W4413847202","doi":"10.1109/tii.2025.3598512","title":"Empowering Multirobot Flocking in Complex Environments via Effective Communication: A Deep Reinforcement Learning Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Flocking (texture); Reinforcement learning; Computer science; Artificial intelligence; Reinforcement; Human–computer interaction; Distributed computing; Engineering","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":[0.0005502365,0.000666439,0.0005403566,0.0002564359,0.0002849931,0.0004222496,0.001060625,0.0007456146,0.000791444],"category_scores_gemma":[0.001268834,0.000292912,0.00032945,0.0001723326,0.0006885267,0.0007070515,0.001043257,0.001071882,0.0001323029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004980813,"about_ca_system_score_gemma":0.0006716106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002823168,"about_ca_topic_score_gemma":0.002854656,"domain_scores_codex":[0.9998246,0.00004896522,0.000007164949,0.0000397502,0.00004183898,0.00003775639],"domain_scores_gemma":[0.9995182,0.0002289142,0.00008527173,0.00003858924,0.00007624205,0.00005275047],"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":[0.00004243438,0.00005668392,0.0007764501,0.00004038311,0.00003219116,0.00006250223,0.00007006369,0.949605,0.005204043,0.006795658,0.0005982766,0.03671629],"study_design_scores_gemma":[0.000002884637,0.00001629783,0.00004467231,0.000001745171,0.00000311922,0.000004465299,0.000003575586,0.9982882,0.0002798033,0.001231763,0.0001215832,0.000001809706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04548225,0.0002541585,0.9514819,0.0002529592,0.00003173183,0.00002513496,0.00001620133,0.0002483939,0.002207243],"genre_scores_gemma":[0.9408277,0.000155144,0.05697347,0.0001189896,0.00002755157,0.00005707512,0.0000327233,0.00003344026,0.001773885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002823168,"threshold_uncertainty_score":0.005613506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03551621687307693,"score_gpt":0.2762670482996766,"score_spread":0.2407508314265997,"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."}}