{"id":"W4402067153","doi":"10.1002/aic.18584","title":"A reinforcement learning approach with masked agents for chemical process flowsheet design","year":2024,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Process (computing); Reinforcement; Process engineering; Computer science; Biochemical engineering; Engineering; Materials science; Artificial intelligence; Composite material; Programming language","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":[0.001253959,0.0006652374,0.0005390507,0.00025931,0.0003210118,0.0006298741,0.0009455145,0.00078482,0.001849144],"category_scores_gemma":[0.002606084,0.0003835535,0.0003979556,0.0001480055,0.000960007,0.0006189458,0.001001385,0.001000486,0.0001971183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006490165,"about_ca_system_score_gemma":0.001307635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003169019,"about_ca_topic_score_gemma":0.002353276,"domain_scores_codex":[0.9995286,0.0002075053,0.00001974914,0.00007365669,0.0001169852,0.00005344501],"domain_scores_gemma":[0.998765,0.0007489572,0.0001582526,0.00009408045,0.0001462621,0.00008760026],"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":[0.0000710588,0.00004336122,0.000245244,0.00002062896,0.00001529412,0.00002890544,0.00003216971,0.9808399,0.001782844,0.006142017,0.0001057373,0.01067283],"study_design_scores_gemma":[0.000009031994,0.00002511034,0.00001641605,0.000001290693,0.000001844854,0.000002136567,0.000001665679,0.9984755,0.0003023917,0.001049918,0.0001132296,0.00000142875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04465505,0.00007480275,0.9523359,0.0001660556,0.00002708409,0.00007955021,0.00001533678,0.000235863,0.002410331],"genre_scores_gemma":[0.8735911,0.0000438315,0.1243873,0.0000812173,0.00001591735,0.000103237,0.00001716419,0.00001911423,0.001741029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003169019,"threshold_uncertainty_score":0.006631672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193525708122232,"score_gpt":0.2493957199066536,"score_spread":0.2274604628254313,"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."}}