{"id":"W3170007158","doi":"10.1109/tcomm.2021.3086535","title":"Caching Transient Content for IoT Sensing: Multi-Agent Soft Actor-Critic","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Age of Information Optimization","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Science, Technology and Innovation Commission of Shenzhen Municipality; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Cache; Computer science; Upload; Cloud computing; Markov decision process; Reinforcement learning; Distributed computing; Computer network; Smart Cache; Enhanced Data Rates for GSM Evolution; Cache algorithms; Real-time computing; Markov process; CPU cache; Telecommunications; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001482574,0.001063924,0.001226954,0.0003227108,0.0003261916,0.001105481,0.001184622,0.001236788,0.001050442],"category_scores_gemma":[0.003780986,0.0004745638,0.0005045088,0.0003420713,0.00115606,0.0007870541,0.0009171029,0.001559183,0.0001708544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060054,"about_ca_system_score_gemma":0.001088835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009357079,"about_ca_topic_score_gemma":0.006323832,"domain_scores_codex":[0.9994727,0.0001995013,0.00002594065,0.0001171301,0.0001048975,0.00007993347],"domain_scores_gemma":[0.9975078,0.001708412,0.00028797,0.0000871415,0.0002824742,0.0001261623],"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":[0.00004259624,0.00001908955,0.0004125442,0.00002110147,0.00002269292,0.00004613281,0.00001874353,0.9939027,0.000324088,0.002008526,0.0001862764,0.002995428],"study_design_scores_gemma":[0.000003459985,0.000006292186,0.00002530419,0.000001398991,0.000002530944,0.000002133251,0.000001994819,0.9994814,0.00003927189,0.0004061127,0.00002912442,0.000001098219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06375908,0.0005737327,0.929651,0.0006610349,0.0001294588,0.00005698112,0.00005841311,0.0003509292,0.004759301],"genre_scores_gemma":[0.9855494,0.0001381587,0.01239171,0.0001171328,0.00002984313,0.00005389898,0.00003895572,0.00002147809,0.001659397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009357079,"threshold_uncertainty_score":0.01860517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1064839323359109,"score_gpt":0.3054570176877371,"score_spread":0.1989730853518262,"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."}}