{"id":"W3138688069","doi":"10.3390/s21072302","title":"Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic control and management","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Department of Education and Knowledge","keywords":"Reinforcement learning; Computer science; SIGNAL (programming language); Queue; Duration (music); Artificial intelligence; Control (management); Process (computing); Set (abstract data type); Traffic signal; Real-time computing; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008076008,0.0001413283,0.0001623313,0.00006186804,0.00008367946,0.00004534407,0.00004328955,0.00003233981,0.0001950482],"category_scores_gemma":[0.00001380349,0.0001561449,0.00008323979,0.0001012278,0.00001136762,0.00006306432,0.00001331466,0.0001711726,0.00004981242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001092668,"about_ca_system_score_gemma":0.00001296046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007505292,"about_ca_topic_score_gemma":0.00002410359,"domain_scores_codex":[0.999186,0.00003505569,0.0001696715,0.0001623406,0.0001646355,0.0002822549],"domain_scores_gemma":[0.9997149,0.00003384568,0.00002817059,0.000121179,0.00003426226,0.00006771733],"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":[0.00001197744,0.000007999691,0.000007589481,0.00003374395,0.00009249333,0.00006961228,0.0001187813,0.968038,0.008524841,0.00006360222,0.0001132161,0.02291812],"study_design_scores_gemma":[0.0008241586,0.00002032086,0.0001479542,0.00001702206,0.00006347569,0.00002169264,0.000421203,0.9792919,0.002080102,0.000003095523,0.01693557,0.0001735407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9225256,0.0003299364,0.0732041,0.0001263467,0.0005181069,0.000174439,9.359172e-7,0.0005658178,0.00255467],"genre_scores_gemma":[0.9986658,0.00005113795,0.000233135,0.00003837599,0.0001571934,0.000005034361,0.00000930907,0.00002838112,0.0008116419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07614014,"threshold_uncertainty_score":0.6367404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01100900227509839,"score_gpt":0.2072529888691372,"score_spread":0.1962439865940388,"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."}}