{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007030536,0.00076881,0.0007048196,0.0002825559,0.0001919154,0.0006342288,0.001085192,0.0006883292,0.001427733],"category_scores_gemma":[0.00146831,0.0002903213,0.0003789479,0.0002420274,0.0006029041,0.0006029342,0.0006753326,0.00122416,0.0002068222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009028108,"about_ca_system_score_gemma":0.001058507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00912424,"about_ca_topic_score_gemma":0.008025481,"domain_scores_codex":[0.9996732,0.00006901997,0.00001564302,0.00009505764,0.00007725052,0.00006985141],"domain_scores_gemma":[0.9994251,0.0002658776,0.000074765,0.00004166803,0.0001413126,0.00005125822],"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":[0.00006314525,0.0000793802,0.0007455089,0.00003476871,0.00003534616,0.00003747331,0.00002573225,0.9500096,0.001618824,0.002344422,0.0006647325,0.04434103],"study_design_scores_gemma":[0.000003744932,0.00001514336,0.00003873318,0.000001461228,0.000002297507,0.000002361732,0.000001167096,0.9991197,0.0001487633,0.0005845993,0.00008079758,0.000001277357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05484178,0.0005705801,0.9389641,0.0003230172,0.00009616178,0.00005128931,0.00008400812,0.001288494,0.003780475],"genre_scores_gemma":[0.9661103,0.0001163209,0.03176849,0.0001596097,0.000028272,0.00005864142,0.00009792861,0.00003015891,0.001630294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00912424,"threshold_uncertainty_score":0.01814228,"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."}}