{"id":"W2182877511","doi":"10.82308/46241","title":"Optimal time scales for reinforcement learning behaviour strategies","year":2010,"lang":"en","type":"article","venue":"Open MIND","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; Computer science; Artificial intelligence; Temporal difference learning; Formalism (music); Gradient descent; Representation (politics); Q-learning; Scale (ratio); Machine learning; Mathematical optimization; Artificial neural network; 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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005758661,0.0001669389,0.0001787071,0.0000696505,0.0002919795,0.001653303,0.001802129,0.00009230382,0.0007928176],"category_scores_gemma":[0.00007931678,0.0001600576,0.00006580196,0.000128526,0.00006195775,0.00126463,0.0007326409,0.0003290637,0.0007695582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000220582,"about_ca_system_score_gemma":0.0001489887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001356996,"about_ca_topic_score_gemma":0.000007379906,"domain_scores_codex":[0.9986631,0.00002652504,0.0002931438,0.0003824372,0.0002588575,0.0003759336],"domain_scores_gemma":[0.9990227,0.0001030483,0.0001692339,0.0004954917,0.0001046668,0.0001048689],"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":[0.00001270364,0.00001951833,0.0003240411,0.000004399168,0.000018006,0.000004969489,0.0006646303,0.9670671,0.005702648,0.003340407,0.0003684394,0.02247314],"study_design_scores_gemma":[0.0006341233,0.0003907452,0.0004394311,0.00001884412,0.00001519102,0.00001300501,0.0001973194,0.9299448,0.006388408,0.00004712696,0.0615698,0.0003412721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08491374,0.00000384285,0.8850424,0.0002105311,0.0003032976,0.0006198084,9.010674e-7,0.00001810793,0.02888734],"genre_scores_gemma":[0.6002663,0.000001083329,0.3792535,0.00002258139,0.00006745963,0.0000358841,0.00002298234,0.00001392124,0.02031621],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5153526,"threshold_uncertainty_score":0.9993831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02682287044972681,"score_gpt":0.3060654353432031,"score_spread":0.2792425648934763,"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."}}