{"id":"W7108223307","doi":"10.1109/tvt.2025.3639149","title":"Toward Requested VR QoE Using Multi-Objective Reinforcement Learning","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Quality of experience; Wireless; Virtual reality; Bandwidth (computing); Wireless network; Resource (disambiguation); Selection (genetic algorithm); Resource management (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":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007407273,0.0007164184,0.0008339537,0.002286459,0.001329863,0.0003166226,0.00137834,0.001085271,0.00007682922],"category_scores_gemma":[0.00005886725,0.0008120627,0.000450824,0.003836884,0.0005419699,0.0007119724,0.00004845886,0.002639054,0.0001305029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001176193,"about_ca_system_score_gemma":0.0008494314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003816976,"about_ca_topic_score_gemma":0.00004790613,"domain_scores_codex":[0.9950204,0.0005406894,0.001180106,0.001522439,0.0005943729,0.001142017],"domain_scores_gemma":[0.9971796,0.0001502911,0.0003933851,0.001554679,0.0005812111,0.000140871],"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":[0.0001422227,0.001599483,0.0000541027,0.000367423,0.001399581,0.0004003803,0.002625226,0.663476,0.03806943,0.01110885,0.00003011159,0.2807271],"study_design_scores_gemma":[0.001775521,0.0007353186,0.00002762155,0.0006648796,0.0003039364,0.00007479972,0.001313551,0.7040204,0.287585,0.0006599296,0.002152148,0.0006868123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01010042,0.0006171971,0.9810207,0.003532651,0.002222601,0.001103309,0.000005530743,0.0009451305,0.0004524858],"genre_scores_gemma":[0.9652202,0.0003200274,0.03125434,0.0007430387,0.00003191746,0.0001493602,0.000002613058,0.00004227109,0.00223625],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9551198,"threshold_uncertainty_score":0.9999703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04300119225724845,"score_gpt":0.3260113996631552,"score_spread":0.2830102074059067,"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."}}