{"id":"W4398787736","doi":"10.1109/access.2024.3405334","title":"A Reinforcement Learning Congestion Control Algorithm for Smart Grid Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; Agència de Gestió d'Ajuts Universitaris i de Recerca; Generalitat de Catalunya; European Commission","keywords":"Reinforcement learning; Computer science; Network congestion; Smart grid; Algorithm; Artificial intelligence; Distributed computing; Computer network; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001661485,0.0008154017,0.001169461,0.0006133415,0.0005044497,0.0009180199,0.00169474,0.00124657,0.003210047],"category_scores_gemma":[0.004431106,0.0004153019,0.0004009135,0.000440962,0.0008124297,0.001125786,0.0009485861,0.001554846,0.0003976082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001667845,"about_ca_system_score_gemma":0.001975467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01207748,"about_ca_topic_score_gemma":0.008396235,"domain_scores_codex":[0.9995428,0.0001348751,0.00002578638,0.0001199463,0.00008763622,0.00008904948],"domain_scores_gemma":[0.9984107,0.0009689716,0.0001431512,0.00006107283,0.0003161485,0.00009994279],"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":[0.00006043957,0.00004262483,0.00036221,0.00002843537,0.00001668126,0.0000256125,0.00002601941,0.9612813,0.0004489949,0.004485456,0.001173252,0.03204893],"study_design_scores_gemma":[0.000006985777,0.000007387077,0.00001592078,0.000001490688,0.000001177828,0.000002037797,0.000001351065,0.9990185,0.0000667651,0.0007667267,0.0001103193,0.000001227433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02507251,0.0003701261,0.9691473,0.0004313733,0.0001117093,0.0001026506,0.00006019867,0.001018023,0.00368612],"genre_scores_gemma":[0.8528582,0.0001849034,0.1410788,0.0002553975,0.00006973103,0.0002396073,0.0002025512,0.0001189765,0.004991679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01207748,"threshold_uncertainty_score":0.02401435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01127201187616491,"score_gpt":0.2557219067097106,"score_spread":0.2444498948335457,"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."}}