{"id":"W3096189530","doi":"10.1155/2020/8813467","title":"Ramp Metering for a Distant Downstream Bottleneck Using Reinforcement Learning with Value Function Approximation","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Missouri University of Science and Technology; U.S. Department of Transportation","keywords":"Bottleneck; Metering mode; Downstream (manufacturing); Reinforcement learning; Upstream (networking); Traverse; Computer science; Control theory (sociology); Nonlinear system; Traffic congestion; Traffic flow (computer networking); Real-time computing; Simulation; Engineering; Control (management); Artificial intelligence; Transport engineering; Computer network","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":[0.0008764497,0.0007108262,0.001321589,0.0003562091,0.0003390987,0.0008292351,0.0009772032,0.001166876,0.001638157],"category_scores_gemma":[0.002392562,0.0004845446,0.0005158576,0.0003071688,0.0008398507,0.0008196633,0.0009649019,0.001417394,0.0001939045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009299328,"about_ca_system_score_gemma":0.001306065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009492746,"about_ca_topic_score_gemma":0.005951354,"domain_scores_codex":[0.9996645,0.00009964244,0.0000178555,0.00008814858,0.00006210728,0.00006767887],"domain_scores_gemma":[0.9989484,0.0006871111,0.000137931,0.00004483968,0.0001128289,0.00006899006],"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":[0.00003459887,0.00003024232,0.0004425666,0.00002386467,0.00001104582,0.00006274841,0.00001959665,0.9884411,0.0005190108,0.001546405,0.00018174,0.008687105],"study_design_scores_gemma":[0.000003542699,0.000008378362,0.00002658452,0.000001427293,0.000001496064,0.000002499229,0.000001915822,0.9993912,0.00006812609,0.0004606575,0.00003296968,0.00000118658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08204397,0.000340352,0.9132459,0.0003316453,0.00005287719,0.00007497782,0.00003028939,0.0005493656,0.003330619],"genre_scores_gemma":[0.9614137,0.00007626542,0.03698044,0.00007071024,0.00001671237,0.00006501045,0.00003640223,0.0000227597,0.001317968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009492746,"threshold_uncertainty_score":0.018875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009668714866445945,"score_gpt":0.2029283758450863,"score_spread":0.1932596609786403,"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."}}