{"id":"W4391467417","doi":"10.1109/tte.2024.3361462","title":"Safe Reinforcement Learning for Energy Management of Electrified Vehicle With Novel Physics-Informed Exploration Strategy","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Reinforcement; Computer science; Psychology; Artificial intelligence; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009082228,0.0002134799,0.0001779367,0.0002621589,0.0001276513,0.00003882387,0.0001038873,0.0001095898,0.000009962185],"category_scores_gemma":[6.411333e-7,0.0002142555,0.00009271257,0.0008773492,0.0000333028,0.0004569739,1.635101e-8,0.0002354306,0.000003773658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001610143,"about_ca_system_score_gemma":0.00006556113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001553722,"about_ca_topic_score_gemma":0.00003085217,"domain_scores_codex":[0.9987874,0.000007825814,0.0004258067,0.0002585008,0.0002468716,0.0002735652],"domain_scores_gemma":[0.9995449,0.00007550856,0.00006932349,0.0001685279,0.0001056507,0.00003605503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000141995,0.00005685375,0.000001198007,0.0003226825,0.000200907,8.525532e-7,0.0001514285,0.7082579,0.1319956,0.01774205,0.00002794199,0.1411006],"study_design_scores_gemma":[0.0005758124,0.0004614371,0.00007740595,0.00009187065,0.0001360893,8.443376e-7,0.00009274842,0.2467391,0.7508401,0.0004034044,0.0003819069,0.000199231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01414637,0.0001753823,0.9837945,0.00005302217,0.00008052905,0.000522227,0.00001864873,0.0008787721,0.0003305747],"genre_scores_gemma":[0.9958944,0.001405178,0.001337544,0.000008130955,0.00002012447,0.0006296166,0.0002038505,0.00005302205,0.0004481421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9824569,"threshold_uncertainty_score":0.8737086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02053222955237301,"score_gpt":0.2337469359446955,"score_spread":0.2132147063923225,"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."}}