{"id":"W3090944066","doi":"10.1109/tccn.2020.3027696","title":"UAV-Assisted Wireless Energy and Data Transfer With Deep Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reinforcement learning; Computer science; Markov decision process; Wireless; Partially observable Markov decision process; Real-time computing; Data transmission; Markov process; Distributed computing; Markov chain; Computer network; Markov model; Artificial intelligence; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007123254,0.0006622684,0.0008404847,0.0002164099,0.0003161544,0.0005930719,0.0009043568,0.0009070828,0.001568109],"category_scores_gemma":[0.001565152,0.0002963663,0.0003900184,0.0002621004,0.0006841124,0.000780085,0.0009184622,0.001158723,0.0001575339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008094932,"about_ca_system_score_gemma":0.001126738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007338695,"about_ca_topic_score_gemma":0.005709926,"domain_scores_codex":[0.9997358,0.00007286971,0.00001271712,0.00006181753,0.00005762863,0.00005906625],"domain_scores_gemma":[0.9993137,0.0003840556,0.0001024547,0.00003803094,0.0001163479,0.00004532781],"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":[0.00002969049,0.00003169733,0.0003436837,0.00002683901,0.00001161527,0.00003888634,0.0000158924,0.9878312,0.000512989,0.002106723,0.0002936792,0.0087571],"study_design_scores_gemma":[0.000002790503,0.00001046812,0.00002203686,0.00000134806,0.000001383878,0.000003028259,0.000001672745,0.9993611,0.00008577597,0.0004573887,0.00005186653,0.000001095329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05657794,0.0005578503,0.9370514,0.0004436777,0.00007656498,0.0000592127,0.00005671028,0.0003617051,0.00481487],"genre_scores_gemma":[0.9785639,0.0001100142,0.01955876,0.00009849087,0.00001272049,0.0000568297,0.00003659612,0.00001462051,0.001548049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007338695,"threshold_uncertainty_score":0.01459199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0449217732959111,"score_gpt":0.2440623563199031,"score_spread":0.199140583023992,"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."}}