{"id":"W4389074848","doi":"10.1007/s10994-023-06460-4","title":"Multi-agent reinforcement learning for fast-timescale demand response of residential loads","year":2023,"lang":"en","type":"article","venue":"Machine Learning","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Group for Research in Decision Analysis; Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Samsung; Institut de Valorisation des Données; Microsoft Research","keywords":"Reinforcement learning; Computer science; Demand response; Renewable energy; Distributed computing; Power (physics); Artificial intelligence; Engineering; Electrical engineering; Electricity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001457997,0.0005963319,0.0009883186,0.0003138288,0.0003637735,0.0005966497,0.0008768711,0.0009386248,0.00169081],"category_scores_gemma":[0.004661393,0.0004017588,0.000301276,0.0002631495,0.0005675185,0.0006291518,0.0008075986,0.001218517,0.0002275381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008159641,"about_ca_system_score_gemma":0.0008360525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009679208,"about_ca_topic_score_gemma":0.008222916,"domain_scores_codex":[0.9996424,0.0001469459,0.00001569412,0.00006563083,0.00005962934,0.0000697512],"domain_scores_gemma":[0.997772,0.001622584,0.0001868442,0.00007514974,0.0002432155,0.0001000732],"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":[0.00005049117,0.00003448262,0.0002425021,0.00001458415,0.000008901568,0.00002510715,0.00001434969,0.9918517,0.0002813371,0.0009499789,0.0002573452,0.006269122],"study_design_scores_gemma":[0.000002542882,0.000004303883,0.00002043321,4.420262e-7,5.222118e-7,0.000001008806,8.749298e-7,0.9997296,0.00003126503,0.0001935902,0.00001491644,5.218873e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.152268,0.0003090852,0.8429583,0.0005321281,0.0001044843,0.00007951407,0.00008169483,0.0005914303,0.003075365],"genre_scores_gemma":[0.9890366,0.0000283935,0.009803445,0.00003608261,0.0000122807,0.00003215037,0.00002777017,0.00001664621,0.001006626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009679208,"threshold_uncertainty_score":0.01924574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256083026442792,"score_gpt":0.2416896789584488,"score_spread":0.2291288486940209,"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."}}