{"id":"W4401211382","doi":"10.1109/tce.2024.3436824","title":"Deep Reinforcement Learning-Based Computation Offloading for Mobile Edge Computing in 6G","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Mobile edge computing; Computation offloading; Computation; Edge computing; Artificial intelligence; Mobile computing; Distributed computing; Enhanced Data Rates for GSM Evolution; Computer network; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005331786,0.0002608384,0.0002483056,0.0005031666,0.0004040865,0.0003085821,0.0003435465,0.0001155346,0.00000423971],"category_scores_gemma":[0.000008230877,0.0002883638,0.0001773505,0.0008463282,0.00003693443,0.0002819183,0.000004907034,0.0006739793,0.00005229942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004694975,"about_ca_system_score_gemma":0.000389001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001963376,"about_ca_topic_score_gemma":0.00002511613,"domain_scores_codex":[0.9978946,0.00008962526,0.00046508,0.0005813016,0.0002532773,0.0007161081],"domain_scores_gemma":[0.9987941,0.0007155496,0.00007766249,0.0002311562,0.00009728074,0.00008428067],"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":[0.00001670432,0.00005813418,0.000009373081,0.00007948662,0.00003797302,0.0000052474,0.0005183332,0.8033799,0.0003049614,0.0002230522,0.0001461169,0.1952207],"study_design_scores_gemma":[0.0006153264,0.0003780733,0.000007292672,0.0001449396,0.00002609429,0.00001013929,0.00001486175,0.9813427,0.007990781,0.000181788,0.008984569,0.0003034434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008290409,0.001109182,0.9845751,0.0001521622,0.00459724,0.0006069798,2.595731e-7,0.0005525155,0.0001161345],"genre_scores_gemma":[0.9912686,0.00003547911,0.008143891,0.0001453261,0.000161591,0.00008778305,0.000008448542,0.00003644922,0.0001124403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9829782,"threshold_uncertainty_score":0.9999568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388678682246187,"score_gpt":0.2716445906189046,"score_spread":0.2577578037964428,"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."}}