{"id":"W3190005886","doi":"10.1109/icc42927.2021.9500625","title":"Mobility Aware Channel Allocation for 5G Vehicular Networks using Multi-Agent Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reinforcement learning; Computer science; Markov decision process; Resource allocation; Throughput; Channel (broadcasting); Q-learning; Network packet; Distributed computing; Artificial intelligence; Markov process; Computer network; Wireless; Telecommunications","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":[0.00037995,0.0002566972,0.0002638498,0.00004534059,0.0001838811,0.00007312508,0.0001201311,0.0001812447,0.00008825494],"category_scores_gemma":[0.00005155846,0.0002807243,0.0001668515,0.0002339162,0.00001899657,0.0001574097,0.00004626833,0.0002923453,0.0000124002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003153133,"about_ca_system_score_gemma":0.00004180129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003553015,"about_ca_topic_score_gemma":0.00007025454,"domain_scores_codex":[0.9984289,0.00005402343,0.0003926203,0.0003600382,0.0002011515,0.0005633034],"domain_scores_gemma":[0.9991765,0.00005875973,0.00005175237,0.0003754935,0.0001935901,0.0001438931],"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":[0.000005278381,0.00002885256,0.0001233802,0.0001143648,0.00009743932,0.000009947006,0.00009144089,0.9969636,0.001429948,0.00004610197,0.0001860189,0.0009035982],"study_design_scores_gemma":[0.0005730923,0.00002405051,0.0002094085,0.00006852169,0.0000474694,0.00001210101,0.0001520867,0.9907749,0.005066123,0.00001013331,0.002743133,0.0003189453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05969355,0.0009161314,0.9375978,0.00003616707,0.0005273118,0.0005899096,0.000001102959,0.0004277152,0.0002103263],"genre_scores_gemma":[0.9932776,0.0001157754,0.005307304,0.00009767224,0.0002695622,0.0001096296,0.000220077,0.00007546194,0.0005269098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.933584,"threshold_uncertainty_score":0.9999645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02571097120381963,"score_gpt":0.248724426905142,"score_spread":0.2230134557013224,"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."}}