{"id":"W3090667645","doi":"10.1109/tits.2020.3025832","title":"Dynamic Pricing for Differentiated PEV Charging Services Using Deep Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Dynamic pricing; Quality of service; Service (business); Interdependence; Service quality; Popularity; Operations research; Computer network; Engineering; Artificial intelligence; Business; Marketing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006704846,0.000274929,0.0002923202,0.0001559257,0.0002295616,0.0000768773,0.0001273238,0.0001254311,0.00006091855],"category_scores_gemma":[7.130382e-7,0.0002828326,0.0001610042,0.0003523625,0.00001045162,0.0001962006,1.53181e-7,0.0003110265,0.00001222994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00014404,"about_ca_system_score_gemma":0.00001361398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005329472,"about_ca_topic_score_gemma":0.00003575623,"domain_scores_codex":[0.9985223,0.00002196578,0.000608936,0.0002728016,0.0002472863,0.0003266941],"domain_scores_gemma":[0.9995227,0.00004379581,0.0001010663,0.0001069902,0.00009222516,0.000133229],"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":[0.00004000222,0.0000105015,0.00005181832,0.0008016129,0.0001420339,0.000001438151,0.003146147,0.9730971,0.02017259,0.00003406945,0.000002129661,0.002500557],"study_design_scores_gemma":[0.0003394595,0.0001201793,0.00007843399,0.0001721074,0.0001118074,0.000002394799,0.001269752,0.9676251,0.02968867,0.000004789953,0.0003164615,0.0002707839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1836154,0.0001893542,0.8145782,0.00001673135,0.0005819906,0.0005836938,0.00001828502,0.0003959544,0.00002047009],"genre_scores_gemma":[0.9989384,0.000134346,0.0005388579,0.00004736337,0.0000535792,0.00008303664,0.00008817165,0.00007577911,0.0000404583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8153231,"threshold_uncertainty_score":0.9999624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01448041719120622,"score_gpt":0.224907192741706,"score_spread":0.2104267755504998,"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."}}