{"id":"W2994992444","doi":"10.1109/wcsp.2019.8927868","title":"Deep Reinforcement Learning for Scheduling in Cellular Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Reinforcement learning; Scheduling (production processes); Robustness (evolution); Artificial intelligence; Job shop scheduling; Artificial neural network; Distributed computing; Flexibility (engineering); Machine learning; Computer network; 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":[0.0006679587,0.0005808223,0.0007041623,0.0002538244,0.0002715401,0.0004989916,0.0006128028,0.0006558023,0.001345519],"category_scores_gemma":[0.00207849,0.0002667364,0.0002467889,0.0002997826,0.0006595162,0.0004979247,0.0005773808,0.0008642186,0.0001217997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001180392,"about_ca_system_score_gemma":0.001148603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01330936,"about_ca_topic_score_gemma":0.0104778,"domain_scores_codex":[0.9997796,0.00007196953,0.000007738243,0.00003752385,0.00003684948,0.00006626762],"domain_scores_gemma":[0.9993365,0.0004057728,0.00007298517,0.00002687057,0.0001114295,0.00004642677],"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":[0.00002116126,0.00001170058,0.0002660534,0.00001226435,0.000007857951,0.00001520729,0.000009820425,0.9893662,0.0002697428,0.002937485,0.0002220194,0.006860583],"study_design_scores_gemma":[0.000001744536,0.000003492089,0.00002151415,7.540615e-7,9.708351e-7,9.038043e-7,0.00000112983,0.9990904,0.00004095459,0.0007921342,0.00004541274,5.749413e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08612754,0.00077816,0.9064682,0.0006623105,0.00008975716,0.00003656313,0.00005875974,0.0003524603,0.005426294],"genre_scores_gemma":[0.979925,0.0001583292,0.01779362,0.00008102098,0.000021875,0.00003652793,0.00003291038,0.00001531494,0.001935449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01330936,"threshold_uncertainty_score":0.02646381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006223536284843034,"score_gpt":0.202033234968348,"score_spread":0.1958096986835049,"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."}}