{"id":"W4392843740","doi":"10.1109/twc.2023.3323554","title":"Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reinforcement learning; Scale (ratio); Artificial intelligence; Machine learning; Human–computer interaction; Distributed computing","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":[],"consensus_categories":[],"category_scores_codex":[0.0002272112,0.0002142866,0.000235048,0.0002421204,0.0003159516,0.000135338,0.0004890296,0.0001238618,0.00002807692],"category_scores_gemma":[0.000001988107,0.0002304237,0.00009089505,0.0008699502,0.0000907819,0.0001685297,0.000008106666,0.0009514302,0.00008158595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001790419,"about_ca_system_score_gemma":0.00003415719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006237881,"about_ca_topic_score_gemma":0.0002217611,"domain_scores_codex":[0.9987848,0.0001243197,0.0004294729,0.0002341896,0.0001415385,0.0002857141],"domain_scores_gemma":[0.9989594,0.0002253274,0.00003092233,0.0006727773,0.00003724749,0.00007433903],"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":[0.000007592694,0.00009954043,0.000004290496,0.00002673197,0.00005779289,0.0000017537,0.002406313,0.9620249,0.0003362416,0.00004811045,0.00002614108,0.03496065],"study_design_scores_gemma":[0.0002448281,0.00003185856,0.00001660992,0.0001803715,0.00002299496,0.000003705829,0.0003241115,0.9976814,0.0008394428,0.000002893429,0.0004239177,0.0002278715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00526694,0.0005746836,0.9915198,0.00004782662,0.0003721213,0.0004580336,0.000003953638,0.0003909406,0.001365683],"genre_scores_gemma":[0.9890054,0.001166189,0.00930702,0.00001350468,0.00003799648,0.0001608736,0.00003271657,0.00005176743,0.000224456],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9837385,"threshold_uncertainty_score":0.9396406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01915292420344007,"score_gpt":0.2617181216390526,"score_spread":0.2425651974356126,"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."}}