{"id":"W3123020218","doi":"10.1109/globecom42002.2020.9322180","title":"Slice Reconfiguration based on Demand Prediction with Dueling Deep Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Control reconfiguration; Computer science; Markov decision process; Revenue; Q-learning; Convergence (economics); Process (computing); Artificial intelligence; Artificial neural network; Deep learning; Service (business); Distributed computing; Operations research; Markov process; Engineering; Embedded system","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":[0.0001405719,0.00009852666,0.00008843495,0.00003697243,0.0001517388,0.00014314,0.0001599033,0.00003904035,0.00006406868],"category_scores_gemma":[0.00004306552,0.0000766247,0.00002386574,0.0002698411,0.000007322798,0.0003007744,0.00002407427,0.0001423617,0.00003807594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002313891,"about_ca_system_score_gemma":0.00002810956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000109711,"about_ca_topic_score_gemma":0.000004122647,"domain_scores_codex":[0.9991365,0.00003662083,0.0001576891,0.0002828867,0.0002313358,0.0001549812],"domain_scores_gemma":[0.9995362,0.00009133526,0.00006899764,0.0001587265,0.00005367265,0.0000910662],"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":[0.00003196219,0.000006739957,0.001049001,0.000006933356,0.000005576964,0.000003212453,0.0002610183,0.9856861,0.00004843779,0.001434956,0.0003478307,0.01111828],"study_design_scores_gemma":[0.0003968222,0.0006638664,0.0004747207,0.00002758895,0.000004987204,0.000001955596,0.00002817702,0.9957176,0.0006115357,0.00002057376,0.001953993,0.0000981198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00162317,0.00001401922,0.9884398,0.001768478,0.00008403564,0.0001343699,6.614476e-8,0.0004212381,0.00751484],"genre_scores_gemma":[0.9785988,0.000007436564,0.01816112,0.002971743,0.0001236377,0.00001385097,0.00001034128,0.000007697576,0.0001053635],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9769756,"threshold_uncertainty_score":0.3124665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456979652855594,"score_gpt":0.1980700989281832,"score_spread":0.1835003023996273,"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."}}