{"id":"W7030246915","doi":"","title":"Network architecture enforced symmetry","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Gait; Variety (cybernetics); Symmetry (geometry); Work (physics); Task (project management); Space (punctuation); Motion (physics); Action (physics)","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":[0.0007963603,0.0009842649,0.0006788869,0.0004021832,0.0004297094,0.0007727381,0.001038732,0.0008316594,0.00682592],"category_scores_gemma":[0.003677478,0.0002736611,0.0005868216,0.0002364429,0.0005670935,0.001112045,0.00105637,0.001371056,0.001047938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001187061,"about_ca_system_score_gemma":0.001401462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005280257,"about_ca_topic_score_gemma":0.005406744,"domain_scores_codex":[0.999522,0.00008304395,0.00002854413,0.000138176,0.0001248984,0.0001033104],"domain_scores_gemma":[0.9989102,0.000337569,0.0001273545,0.0002048003,0.0003357896,0.00008438188],"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":[0.0002279045,0.0001196296,0.001690825,0.0001291749,0.00005407009,0.0001827386,0.00004425598,0.8536067,0.009971101,0.01360998,0.00453191,0.1158317],"study_design_scores_gemma":[0.00001362374,0.00007235469,0.0002494546,0.00001128309,0.000009638161,0.00003476642,0.000007281998,0.992651,0.002615752,0.00361845,0.0007114987,0.000004862315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2165047,0.0006285128,0.7359071,0.001037633,0.0004537145,0.0002580351,0.0007623003,0.003361275,0.04108676],"genre_scores_gemma":[0.9334535,0.0001798716,0.05595478,0.000277076,0.0000374472,0.00017791,0.0006527393,0.0001538153,0.009112792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00682592,"threshold_uncertainty_score":0.02283502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252967180574079,"score_gpt":0.2315716952647579,"score_spread":0.2190420234590171,"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."}}