{"id":"W4319985622","doi":"10.1002/cjce.24878","title":"Multi‐agent reinforcement learning for process control: Exploring the intersection between fields of reinforcement learning, control theory, and game theory","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta","keywords":"Reinforcement learning; Marl; Controller (irrigation); Computer science; Process (computing); Function (biology); Control (management); Intersection (aeronautics); Reinforcement; Control system; Control theory (sociology); Control engineering; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"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":[0.001737615,0.0005940851,0.0008639523,0.0003649232,0.0003211765,0.001004282,0.0008261726,0.0008883164,0.001126895],"category_scores_gemma":[0.003505455,0.0003396774,0.0005013979,0.0002869835,0.001434028,0.001004456,0.001068615,0.00128183,0.0001112518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008693811,"about_ca_system_score_gemma":0.001033437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003106893,"about_ca_topic_score_gemma":0.001609642,"domain_scores_codex":[0.9994344,0.0002851218,0.00002306008,0.00008596267,0.0001199284,0.00005137998],"domain_scores_gemma":[0.9975457,0.001829113,0.0002192095,0.00009623503,0.0002156686,0.00009405059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004177134,0.00006896407,0.0004594158,0.00007292788,0.00003521903,0.00005007444,0.00004819793,0.960845,0.001472764,0.02354014,0.0001614132,0.01320419],"study_design_scores_gemma":[0.000005115215,0.00002266105,0.00003113809,0.000004171673,0.000002032148,0.000002924106,0.000003198502,0.995604,0.0001425809,0.004055432,0.0001244563,0.000002400051],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03126499,0.0007165993,0.9641102,0.000592593,0.00004244028,0.00004823344,0.000006501294,0.0001042614,0.003114188],"genre_scores_gemma":[0.9369969,0.0003966485,0.06134588,0.0001367653,0.00005616597,0.0001011965,0.000009919499,0.00001989094,0.0009366451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003106893,"threshold_uncertainty_score":0.009189487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02187824374282683,"score_gpt":0.2356329742072273,"score_spread":0.2137547304644005,"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."}}