{"id":"W4210746190","doi":"10.1109/globecom46510.2021.9685978","title":"Deep Reinforcement Learning for URLLC in 5G Mission-Critical Cloud Robotic Application","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Global Communications Conference (GLOBECOM)","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reinforcement learning; Computer science; Energy consumption; Robot; Decoding methods; Mathematical optimization; Latency (audio); Artificial intelligence; Algorithm; Mathematics; Engineering","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":[0.0009653498,0.0006889267,0.0009064228,0.0002295818,0.0003129788,0.0006285926,0.0008417684,0.0009146133,0.001445726],"category_scores_gemma":[0.002469103,0.0002770622,0.0002482916,0.0002410275,0.0008024559,0.0007454278,0.0008189908,0.001282354,0.0001540493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001178772,"about_ca_system_score_gemma":0.001419978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009818121,"about_ca_topic_score_gemma":0.007532946,"domain_scores_codex":[0.9996638,0.00009853853,0.0000122149,0.00007407067,0.00005938852,0.00009190715],"domain_scores_gemma":[0.9990714,0.0005884587,0.0001058901,0.00003694823,0.0001373281,0.00005998181],"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":[0.00005960085,0.00004054147,0.0005613645,0.00003601049,0.00001365431,0.00005889756,0.0000208088,0.9824301,0.0005868631,0.00268327,0.0005368533,0.01297206],"study_design_scores_gemma":[0.000003567211,0.000008712251,0.00003610852,0.000001469472,0.00000144338,0.000002862073,0.000002567607,0.9991624,0.00009145825,0.000631514,0.00005684472,0.000001078677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1062863,0.001266566,0.8848158,0.001225265,0.000112428,0.000071296,0.00007028303,0.000586314,0.005565825],"genre_scores_gemma":[0.9827437,0.000120164,0.01544847,0.0001473951,0.00001858429,0.00003324089,0.00003336908,0.00002215,0.001432911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009818121,"threshold_uncertainty_score":0.01952189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05266965856257814,"score_gpt":0.332331976836987,"score_spread":0.2796623182744088,"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."}}