{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007022856,0.0006409349,0.0007862797,0.0002820053,0.0002543392,0.0005368909,0.0009921815,0.0006258469,0.001409325],"category_scores_gemma":[0.001839805,0.000286223,0.0003490258,0.0002831937,0.0005968351,0.0008711303,0.0006954893,0.001009636,0.0001484796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008253047,"about_ca_system_score_gemma":0.000967945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006069514,"about_ca_topic_score_gemma":0.006388233,"domain_scores_codex":[0.9996831,0.00006526941,0.0000158206,0.00008215063,0.00006100586,0.00009256081],"domain_scores_gemma":[0.9993381,0.0003013417,0.000104508,0.0000517597,0.0001296938,0.0000746176],"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":[0.00009066138,0.00005909421,0.001049897,0.00003203852,0.0000203215,0.00007471761,0.00003099711,0.9510053,0.001841144,0.002780138,0.0008240564,0.0421917],"study_design_scores_gemma":[0.000002863926,0.00001410716,0.00004581853,0.000001428806,0.000002177208,0.000006139433,0.000002046061,0.9988925,0.0002037851,0.0007680547,0.00005936069,0.000001803058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1025891,0.0004760785,0.8923312,0.0003361494,0.00009701064,0.00006437612,0.00006849463,0.0007854255,0.003252148],"genre_scores_gemma":[0.9817137,0.00007472272,0.01706373,0.00009508889,0.0000134246,0.0000296162,0.00005220339,0.00001667569,0.0009407577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006069514,"threshold_uncertainty_score":0.01206839,"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."}}