{"id":"W4387245295","doi":"10.1109/ojcoms.2023.3321310","title":"Hypergraph-Based Resource-Efficient Collaborative Reinforcement Learning for B5G Massive IoT","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Advanced Computing and Algorithms","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Chongqing Research Program of Basic Research and Frontier Technology; King Saud University","keywords":"Computer science; Distributed computing; Reinforcement learning; Hypergraph; Overhead (engineering); Resource (disambiguation); Resource management (computing); Throughput; Markov decision process; Software deployment; Process (computing); Computer network; Markov process; Artificial intelligence; Wireless","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":[0.000825,0.0006632509,0.0009243667,0.0003618847,0.0004993268,0.0007142152,0.001115689,0.0008411743,0.001790793],"category_scores_gemma":[0.002362968,0.0003224058,0.0004099686,0.0004315424,0.0007569639,0.0009177939,0.001119994,0.001202655,0.0001870246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009446024,"about_ca_system_score_gemma":0.001249105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0100878,"about_ca_topic_score_gemma":0.008104334,"domain_scores_codex":[0.9995321,0.0001274479,0.00002011022,0.0001152191,0.00009994023,0.0001051032],"domain_scores_gemma":[0.9988427,0.000673064,0.0001247758,0.00006675882,0.00019327,0.00009935906],"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":[0.0000559814,0.00003966607,0.0005025796,0.00003147134,0.00002594927,0.00007980111,0.00004332633,0.9760334,0.0008285363,0.005687071,0.0007972945,0.01587494],"study_design_scores_gemma":[0.000005090346,0.000009316638,0.00004307715,0.000001518431,0.000003433298,0.00000552437,0.000003715273,0.9981041,0.00008790664,0.001638779,0.00009542803,0.000002064379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04273699,0.0003822065,0.9513004,0.0004408898,0.00007242116,0.00005191402,0.0000571428,0.00038671,0.004571274],"genre_scores_gemma":[0.9742294,0.0001302249,0.02353083,0.0001490678,0.00002276152,0.0000601155,0.00005565907,0.00002382898,0.001798027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0100878,"threshold_uncertainty_score":0.02005816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06694596156319933,"score_gpt":0.3863168683040673,"score_spread":0.3193709067408679,"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."}}