{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001860375,0.0001014375,0.00009905869,0.00006787069,0.0001231345,0.00003570822,0.00006227766,0.00009367903,0.00009475757],"category_scores_gemma":[0.00005412233,0.00009856094,0.00005183566,0.0001071522,0.00001656901,0.000009845274,0.00003530184,0.00009219594,0.00001268228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003421331,"about_ca_system_score_gemma":0.00005071259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001349499,"about_ca_topic_score_gemma":0.00007113501,"domain_scores_codex":[0.9992493,0.00002775579,0.0002214726,0.0002163022,0.00008271007,0.0002024424],"domain_scores_gemma":[0.99962,0.000008089482,0.0001075213,0.0001187122,0.0001026156,0.00004308235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000843642,0.00001639787,0.008081192,0.00001070187,0.00003142027,1.372559e-7,0.00001554075,0.371739,0.6175194,0.00002479672,0.0001557686,0.002397266],"study_design_scores_gemma":[0.001585297,0.000533814,0.01714856,0.00004448434,0.00007350096,0.000007374239,0.000737962,0.748761,0.2108735,0.0001131358,0.01948294,0.0006383898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9548364,0.00005685469,0.04311579,0.0000754053,0.00003012056,0.0003546426,1.447979e-7,0.00001709682,0.001513507],"genre_scores_gemma":[0.9905631,0.00001780901,0.004667279,0.000035572,0.00009815778,0.00006256245,0.00009903069,0.00001777368,0.004438725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4066458,"threshold_uncertainty_score":0.4019199,"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."}}