{"id":"W630726055","doi":"","title":"Comprehensive Analysis of Reinforcement Learning Methods and Parameters for Adaptive Traffic Signal Control","year":2011,"lang":"en","type":"article","venue":"Transportation Research Board 90th Annual MeetingTransportation Research Board","topic":"Traffic control and management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Microsimulation; Intersection (aeronautics); Computer science; Adaptive control; Controller (irrigation); SIGNAL (programming language); Control theory (sociology); Real-time computing; Control engineering; Control (management); Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004360053,0.0003841636,0.00088048,0.002253966,0.0004101706,0.0000579055,0.0003799122,0.0002152453,0.0001928399],"category_scores_gemma":[0.0001462276,0.0003973194,0.0003919169,0.00233968,0.0006108204,0.0003780076,0.000008589374,0.0009422124,0.000005182129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000110285,"about_ca_system_score_gemma":0.0001254019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001791307,"about_ca_topic_score_gemma":0.002501502,"domain_scores_codex":[0.9943109,0.0008343756,0.001234194,0.0007376484,0.001648105,0.001234798],"domain_scores_gemma":[0.9944413,0.002173416,0.0001678371,0.0003310768,0.002342913,0.0005434098],"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":[0.002447611,0.000147529,0.002544601,0.0007269538,0.002928245,0.0000256953,0.0180432,0.9319288,0.002651824,0.003722929,0.0003332096,0.03449939],"study_design_scores_gemma":[0.007034115,0.003514462,0.4409727,0.0002305092,0.001995799,1.906487e-7,0.03349847,0.4979368,0.002761494,0.0005082542,0.01049634,0.001050838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8120457,0.0007815855,0.1821395,0.0001210261,0.0001350164,0.003401591,0.0003461106,0.0003658641,0.0006636617],"genre_scores_gemma":[0.9763581,0.0003901563,0.02182584,0.00002150521,0.00003212816,0.0008784737,0.0002619941,0.00007187163,0.0001599353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4384281,"threshold_uncertainty_score":0.9998479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0771221145176846,"score_gpt":0.3606040578369033,"score_spread":0.2834819433192187,"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."}}