{"id":"W2945334994","doi":"10.65109/iadj1863","title":"Exploration in the Face of Parametric and Intrinsic Uncertainties","year":2019,"lang":"en","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"","keywords":"Reinforcement learning; Parametric statistics; Schedule; Computer science; Face (sociological concept); Quantile; Mathematical optimization; Probability distribution; Artificial intelligence; Mathematics; Statistics","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":[0.0002464294,0.00003728657,0.0000586844,0.0001052286,0.0000117174,0.00005430945,0.0002790383,0.00001631533,0.000005197945],"category_scores_gemma":[0.00005143758,0.00002367494,0.000008682954,0.0004812721,0.00001739813,0.0003434895,0.00007679594,0.00005497731,0.00001946758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000739872,"about_ca_system_score_gemma":0.00001091357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003436134,"about_ca_topic_score_gemma":0.000002202593,"domain_scores_codex":[0.9995461,0.00004003002,0.000109501,0.00008626354,0.0001490041,0.00006909066],"domain_scores_gemma":[0.9995382,0.0001659091,0.00004484228,0.0002239087,0.00002008346,0.000007046947],"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":[0.000002367406,0.00001205495,0.03109245,0.00002215529,0.000004022182,5.617417e-7,0.00518471,0.8057129,0.00005894038,0.147439,0.0001472286,0.01032362],"study_design_scores_gemma":[0.0002261438,0.0001619537,0.02054617,0.0000111078,0.000001332207,0.000001531414,0.001869606,0.9746916,0.0004462005,0.001304312,0.0006649113,0.0000751048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2432199,0.00003098257,0.7529268,0.0005187141,0.00006601998,0.0001415213,2.161167e-8,0.00001561523,0.003080455],"genre_scores_gemma":[0.995353,0.00002325753,0.004070846,0.0001058375,0.000003020069,0.000002187079,2.879175e-7,0.000001134922,0.0004404006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7521331,"threshold_uncertainty_score":0.09654362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02470016581513209,"score_gpt":0.2461940805743528,"score_spread":0.2214939147592207,"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."}}