{"id":"W4405603276","doi":"10.2316/j.2025.206-1106","title":"DEEP REINFORCEMENT LEARNING FOR AUTONOMOUS CONTROL OF MANUFACTURING SYSTEMS IN VOCATIONAL EDUCATION: A COMPARATIVE ANALYSIS, 163-174.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vocational education; Reinforcement learning; Control (management); Autonomous learning; Reinforcement; Artificial intelligence; Mathematics education; Manufacturing engineering; Computer science; Engineering; Psychology; Pedagogy; Structural engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002178886,0.0000836024,0.0001892169,0.0006445935,0.00001948298,0.0001798986,0.00009665893,0.00004667054,0.00001034021],"category_scores_gemma":[0.00001557593,0.0000816523,0.00007936388,0.0001551094,0.0000163235,0.0004854587,0.000005415907,0.0001285003,0.000001049389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001631336,"about_ca_system_score_gemma":0.0000792494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005299627,"about_ca_topic_score_gemma":0.000002590675,"domain_scores_codex":[0.9989648,0.00001398709,0.0006409915,0.00005889372,0.0002508808,0.00007041806],"domain_scores_gemma":[0.9993731,0.0001420548,0.0001617195,0.00003322379,0.0002572936,0.0000325862],"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":[0.000009848056,0.000018248,0.0001222185,0.0001062071,0.0005146541,0.000001035279,0.0005105856,0.9811464,0.00005653666,0.00918927,0.00005484193,0.008270185],"study_design_scores_gemma":[0.0003364161,0.00003630119,0.002303661,0.0002495076,0.00009242382,0.0000262369,0.0004410973,0.994435,0.0005765681,0.0003215174,0.00110787,0.00007341085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01874183,0.0009569281,0.9758558,0.0002043106,0.001264833,0.0001786277,0.000006654456,0.00003384532,0.002757124],"genre_scores_gemma":[0.9984558,0.00005150525,0.001225741,0.000008384202,0.0001292932,0.0000125495,0.00004458583,0.00000627674,0.00006592909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9797139,"threshold_uncertainty_score":0.3329684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138803162539617,"score_gpt":0.2722643675956464,"score_spread":0.2583840513416847,"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."}}