{"id":"W3216891387","doi":"10.1109/etfa45728.2021.9613611","title":"Embedding Reinforcement Learning in Simulation","year":2021,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Reinforcement learning; Embedding; Computer science; Field (mathematics); Visualization; Domain (mathematical analysis); Truck; Discrete event simulation; Artificial intelligence; Industrial engineering; Simulation; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001214006,0.0008630899,0.000760829,0.000646321,0.0003415766,0.001299781,0.001023473,0.0009648504,0.005212381],"category_scores_gemma":[0.006951737,0.0005532673,0.0007282121,0.000440172,0.001176575,0.001293514,0.001427045,0.001365039,0.0006851612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175547,"about_ca_system_score_gemma":0.001203364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006408346,"about_ca_topic_score_gemma":0.004997103,"domain_scores_codex":[0.9991845,0.0004200205,0.00004793586,0.0001234651,0.0001627346,0.00006137782],"domain_scores_gemma":[0.9960327,0.003082382,0.000182252,0.0003470602,0.0002706335,0.00008499277],"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":[0.00001804893,0.0000164722,0.0002569375,0.0000315036,0.00001097422,0.00001653153,0.00002222516,0.979663,0.0001869797,0.01047041,0.0001775494,0.00912948],"study_design_scores_gemma":[0.000004032637,0.000005105564,0.00001607211,0.00000449001,0.00000172354,0.000002564856,0.000002262288,0.9920763,0.0001393725,0.007264118,0.0004822256,0.000001722486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01216648,0.00018756,0.9823054,0.0002186294,0.00004043524,0.0000431089,0.0001046426,0.001390855,0.003542846],"genre_scores_gemma":[0.7190305,0.0004789699,0.2753789,0.0001713574,0.00005924088,0.0003115912,0.0003746573,0.0003723829,0.003822507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006408346,"threshold_uncertainty_score":0.01743722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.012930293854495,"score_gpt":0.2581033303218984,"score_spread":0.2451730364674034,"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."}}