{"id":"W4388958778","doi":"10.1016/j.compchemeng.2023.108511","title":"A practically implementable reinforcement learning control approach by leveraging offset-free model predictive control","year":2023,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Offset (computer science); Reinforcement learning; Model predictive control; Control theory (sociology); Computer science; Nonlinear system; Control engineering; Process (computing); Controller (irrigation); Implementation; Process control; Optimal control; Control (management); Engineering; Mathematical optimization; Artificial intelligence; Mathematics","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":[0.000275622,0.0003978841,0.0004888213,0.0001587735,0.00008480268,0.00008784395,0.0003961783,0.0001420647,0.000006283154],"category_scores_gemma":[0.000175469,0.0004586642,0.0001137439,0.0003787326,0.00002094443,0.0004222395,0.0001263765,0.0005299819,0.00001394503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004004808,"about_ca_system_score_gemma":0.00002243606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004806579,"about_ca_topic_score_gemma":4.018033e-8,"domain_scores_codex":[0.997794,0.0000221822,0.0005669441,0.0004265776,0.0003933322,0.000796937],"domain_scores_gemma":[0.9989577,0.0002660715,0.00008606536,0.0003884342,0.00007479833,0.0002269618],"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":[0.00002831863,0.00001165034,0.0000171619,0.0001050668,0.000191758,0.000003696568,0.0001443488,0.9258414,0.06960156,0.0001504698,0.003547405,0.0003571652],"study_design_scores_gemma":[0.003448188,0.00002899274,0.000003848663,0.00006242123,0.00005245814,0.000009267183,0.00003425501,0.9932874,0.001823211,0.00003215815,0.000796814,0.0004210159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001903904,0.0001330603,0.9942344,0.00009087605,0.000240443,0.0006372884,0.00003066222,0.002263121,0.0004662555],"genre_scores_gemma":[0.9769026,0.00001641332,0.02215369,0.00007707436,0.0001698649,0.0002705889,0.0002268328,0.0001261391,0.00005679881],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9749987,"threshold_uncertainty_score":0.9997865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006049674006604016,"score_gpt":0.1929514060746059,"score_spread":0.1869017320680019,"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."}}