{"id":"W3178554437","doi":"10.1155/2021/6654254","title":"Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Taiwan University; Ministry of Science and Technology, Taiwan","keywords":"Reinforcement learning; Computer science; Public transport; Domain (mathematical analysis); Control (management); Multi-agent system; Intelligent transportation system; Autonomous agent; Q-learning; Real-time computing; Distributed computing; Artificial intelligence; Transport engineering; 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":[0.000828659,0.0006670436,0.0007713124,0.0003463801,0.0004116457,0.000567779,0.0008149337,0.0006575293,0.0008378616],"category_scores_gemma":[0.001600482,0.0003055178,0.0004071535,0.0002337406,0.0006217589,0.0005283152,0.0006436307,0.0007952698,0.0001091547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000720816,"about_ca_system_score_gemma":0.0009872904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01211305,"about_ca_topic_score_gemma":0.006658345,"domain_scores_codex":[0.9996904,0.00009400764,0.00001598109,0.00006902721,0.00007290797,0.00005772458],"domain_scores_gemma":[0.9991909,0.0004087869,0.000148998,0.0000391429,0.0001475701,0.00006459632],"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":[0.00002061264,0.00002092383,0.000244131,0.00001093432,0.00001217261,0.00002282251,0.00001154469,0.9932858,0.0004626036,0.0008928133,0.00008692699,0.004928656],"study_design_scores_gemma":[0.000004141737,0.00001103954,0.00002784063,6.341729e-7,0.000001200322,0.00000165765,0.000001121343,0.9996648,0.00006338095,0.0001906666,0.00003248257,0.000001092518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.090238,0.0002581942,0.9055764,0.0001826452,0.00005811966,0.00007695815,0.00003145854,0.0004116153,0.003166712],"genre_scores_gemma":[0.9823893,0.00005081823,0.01666725,0.00002973664,0.00001097027,0.00005535153,0.00002470512,0.00001006331,0.0007617945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01211305,"threshold_uncertainty_score":0.02408504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113958733179544,"score_gpt":0.2459647498455847,"score_spread":0.2345688765276303,"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."}}