{"id":"W2014505645","doi":"10.1145/2766910","title":"Dynamic terrain traversal skills using reinforcement learning","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Human Motion and Animation","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Terrain; Traverse; Reinforcement learning; Tree traversal; Computer science; Bounding overwatch; Dynamical simulation; Artificial intelligence; Representation (politics); Metric (unit); Pipeline (software); Parametric statistics; Physics engine; Simulation; Algorithm; Engineering; Mathematics","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":[0.0001271384,0.0001176317,0.00008946458,0.0002298079,0.0001215649,0.00003268934,0.00009899242,0.00007621263,0.00008232339],"category_scores_gemma":[0.00001364291,0.0001301075,0.00006969255,0.0002308328,0.00002623867,0.0001516316,0.00000144026,0.0002852779,0.0000484341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001034961,"about_ca_system_score_gemma":0.00001519965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009714968,"about_ca_topic_score_gemma":0.00003230572,"domain_scores_codex":[0.9993621,0.00002378165,0.0001670272,0.0001058004,0.0001801622,0.00016117],"domain_scores_gemma":[0.9996578,0.00002075058,0.00002027241,0.0001795775,0.00003329241,0.00008836927],"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":[0.000004727171,0.00002982825,0.00002248363,0.00001557367,0.00003316146,0.000001646086,0.001385074,0.9916515,0.001200611,0.0001591825,0.00003641337,0.00545979],"study_design_scores_gemma":[0.0006276025,0.0001095591,0.0003495338,0.00004531256,0.00003417625,0.000009545614,0.0005435877,0.9933801,0.000680427,0.0006482666,0.003332807,0.0002391126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1649421,0.00001483091,0.8334223,0.00005454066,0.0002809904,0.00009430047,0.000002501284,0.0003505393,0.0008378683],"genre_scores_gemma":[0.9973097,0.00003645051,0.002326011,0.00008645725,0.00001429197,0.000006012665,0.00001111247,0.00002709622,0.0001829105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8323675,"threshold_uncertainty_score":0.530563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285253422259621,"score_gpt":0.2548259460549967,"score_spread":0.2319734118324005,"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."}}