{"id":"W4300892751","doi":"10.1145/3099564.3099567","title":"Learning locomotion skills using DeepRL","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Human Motion and Animation","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Human–computer interaction","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":[0.0007320526,0.0007562419,0.0005359513,0.0003887237,0.0001932145,0.0006217477,0.0009812402,0.0008161993,0.002929298],"category_scores_gemma":[0.002814691,0.0004902493,0.0004410927,0.0002322652,0.0005014557,0.001031894,0.001127326,0.001207084,0.0009028569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007161082,"about_ca_system_score_gemma":0.000853854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005041401,"about_ca_topic_score_gemma":0.005896822,"domain_scores_codex":[0.999724,0.00005470037,0.00001948383,0.00009362713,0.00006345862,0.00004479867],"domain_scores_gemma":[0.9992636,0.0002918812,0.0001051137,0.0001452923,0.0001304356,0.00006368038],"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":[0.00006665004,0.0001436297,0.001592836,0.00009379892,0.00004342597,0.00006812657,0.00005295193,0.8143582,0.01232108,0.002999907,0.001291421,0.1669679],"study_design_scores_gemma":[0.000005605512,0.00002556859,0.00013222,0.000005974199,0.000002534634,0.000006675896,0.000003950207,0.9966245,0.001285475,0.00170517,0.0001997577,0.000002569885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09670626,0.0003473529,0.894122,0.0002973011,0.00006502375,0.000112944,0.0001800839,0.003951949,0.004216949],"genre_scores_gemma":[0.8199483,0.000198747,0.1748475,0.0001760538,0.00002057697,0.0001610935,0.000449217,0.0001634032,0.004035089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005041401,"threshold_uncertainty_score":0.01002407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251937919428433,"score_gpt":0.270505858693816,"score_spread":0.2453120667509727,"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."}}