{"id":"W2187695640","doi":"","title":"Using label propagation for learning temporally abstract actions in reinforcement learning","year":2013,"lang":"en","type":"article","venue":"","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Abstraction; Reinforcement learning; Construct (python library); Computer science; Artificial intelligence; Machine learning; Key (lock); Programming language","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":[0.004345527,0.001216353,0.001139791,0.0009575895,0.0008358561,0.001050785,0.002171428,0.001760141,0.002607765],"category_scores_gemma":[0.01756108,0.0006136617,0.0007960689,0.0008662848,0.002325661,0.00359904,0.002285272,0.003062048,0.0004284706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001488596,"about_ca_system_score_gemma":0.00152103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004349841,"about_ca_topic_score_gemma":0.006404731,"domain_scores_codex":[0.9983208,0.0008301221,0.00007299065,0.0003005441,0.0003347524,0.000140769],"domain_scores_gemma":[0.987891,0.009724675,0.000665153,0.0007653051,0.0005627442,0.0003911303],"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":[0.0002302837,0.000204228,0.001828022,0.0001072817,0.00007575425,0.0001055619,0.0002097963,0.8637411,0.003163395,0.0389421,0.0009308855,0.09046161],"study_design_scores_gemma":[0.00001399749,0.00002544216,0.00004573474,0.000005208663,0.000005417703,0.000005536841,0.000005730152,0.9747716,0.0006315091,0.02432303,0.0001599066,0.000006825861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01263643,0.00004814876,0.9862467,0.0001435066,0.00001760516,0.00004066525,0.00003182063,0.0003086025,0.0005264596],"genre_scores_gemma":[0.5350056,0.0001189477,0.4624877,0.0001776931,0.00003593587,0.0003257646,0.0001868449,0.0001417959,0.001519753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004349841,"threshold_uncertainty_score":0.02298158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0380446733139703,"score_gpt":0.294542559639028,"score_spread":0.2564978863250577,"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."}}