{"id":"W4285127047","doi":"10.1109/tits.2022.3179893","title":"Adaptive Traffic Signal Control With Deep Reinforcement Learning and High Dimensional Sensory Inputs: Case Study and Comprehensive Sensitivity Analyses","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic control and management","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Reinforcement learning; Sensitivity (control systems); Computer science; Sensory system; SIGNAL (programming language); Adaptive control; Control (management); Artificial intelligence; Machine learning; Engineering; Neuroscience; Psychology; Electronic engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001947274,0.0003112248,0.0004069043,0.0002888458,0.0004954314,0.00004693878,0.0000333836,0.00004000598,0.00004695474],"category_scores_gemma":[3.658754e-7,0.0002973705,0.00006875332,0.0002194483,0.00005526799,0.0001158938,6.616349e-7,0.0003886208,0.000002265519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000112672,"about_ca_system_score_gemma":0.00001619347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006172638,"about_ca_topic_score_gemma":0.001250896,"domain_scores_codex":[0.9982295,0.0002403847,0.0004728281,0.0003771555,0.0004429744,0.0002371441],"domain_scores_gemma":[0.9993345,0.0002376442,0.0000858893,0.0001175236,0.00009792305,0.0001265591],"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":[0.0003882572,0.0001743959,0.00004116927,0.00006917706,0.0009112229,0.001241168,0.003671741,0.9852985,0.0005196335,0.00001225408,0.000002994369,0.00766953],"study_design_scores_gemma":[0.002538034,0.001347082,0.0004017965,0.00003657794,0.0006399103,0.0004751403,0.03281059,0.9610081,0.0002398218,4.262532e-7,0.000112352,0.000390116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5176677,0.0001561831,0.4809477,0.000009821358,0.0001864067,0.0008169221,0.00003961548,0.0001703972,0.000005284046],"genre_scores_gemma":[0.9993964,0.00002907413,0.00004675339,0.00002945296,0.00002039378,0.0003569027,0.00001801801,0.00003787707,0.00006506859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4817288,"threshold_uncertainty_score":0.9999478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02320251107494644,"score_gpt":0.241518189492159,"score_spread":0.2183156784172125,"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."}}