{"id":"W2739573821","doi":"10.3390/make1010002","title":"Learning to Teach Reinforcement Learning Agents","year":2017,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Washington State University; U.S. Department of Agriculture; National Aeronautics and Space Administration; National Science Foundation","keywords":"Reinforcement learning; Advice (programming); Heuristics; Statistic; Action (physics); Variance (accounting); Quality (philosophy); Discounting","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":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001404624,0.0003735767,0.0003364156,0.0003244835,0.003635052,0.001399618,0.0008401965,0.0001610543,0.00009134802],"category_scores_gemma":[0.001760353,0.0003808316,0.0001050437,0.0001959813,0.00006832341,0.001074325,0.000966385,0.001839442,0.0006477701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001490722,"about_ca_system_score_gemma":0.00005948714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000171842,"about_ca_topic_score_gemma":0.00001817365,"domain_scores_codex":[0.9973596,0.0003541156,0.000456608,0.0007595218,0.0004370905,0.0006330081],"domain_scores_gemma":[0.9981717,0.0001913144,0.00054445,0.000613588,0.0001623061,0.000316649],"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":[0.00003218047,0.00004722377,0.05755456,0.00006122237,0.00005602143,0.00002033154,0.004268571,0.7594389,0.001677009,0.001170743,0.0004261852,0.175247],"study_design_scores_gemma":[0.0004936064,0.0005111747,0.01512003,0.00008586251,0.00001773666,0.00003082976,0.0001610905,0.6942566,0.0002362617,0.00001995456,0.2887004,0.0003664314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03505318,0.0002376499,0.8910411,0.001024154,0.001257565,0.0003689679,1.419729e-7,0.0008464083,0.07017086],"genre_scores_gemma":[0.914615,0.0001590606,0.004172842,0.0000586977,0.0002672448,0.00002575031,0.00001502388,0.00004310936,0.08064327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8868682,"threshold_uncertainty_score":0.9998643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02352967150582671,"score_gpt":0.323069848083718,"score_spread":0.2995401765778913,"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."}}