{"id":"W4402809420","doi":"10.1109/twc.2024.3462450","title":"Deep Reinforcement Learning Enables Joint Trajectory and Communication in Internet of Robotic Things","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Trajectory; Joint (building); Artificial intelligence; The Internet; Wireless; Internet of Things; Telecommunications; Computer security; World Wide Web; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002428315,0.0001391245,0.0002144769,0.0003269167,0.0001050579,0.00006639324,0.0003223729,0.00008591253,0.00001916063],"category_scores_gemma":[0.000002896311,0.0001476244,0.00006121091,0.0003088707,0.0001013225,0.0002244366,0.000007336481,0.0005461384,0.00001164664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001089869,"about_ca_system_score_gemma":0.00001893994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002377708,"about_ca_topic_score_gemma":0.0001837847,"domain_scores_codex":[0.9990472,0.0001157695,0.0004611454,0.0001201365,0.0001118043,0.0001439955],"domain_scores_gemma":[0.9989781,0.0002219897,0.00004092014,0.0006857389,0.00003224215,0.00004099554],"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":[0.000001868613,0.00004254771,0.00001072078,0.0002154806,0.00006352306,6.579514e-7,0.0036487,0.9816791,0.004231307,0.001430635,0.00001802986,0.008657413],"study_design_scores_gemma":[0.0001340871,0.00002993285,0.0001003917,0.0007784879,0.00002664425,0.000006239866,0.0004623734,0.9936911,0.004381224,0.00005145382,0.0002055699,0.0001325128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02987574,0.004043775,0.963354,0.00022773,0.0002003824,0.0002763047,0.000001203277,0.0004272649,0.001593586],"genre_scores_gemma":[0.9932677,0.004631368,0.001802704,0.0000103197,0.000003301743,0.00007981508,0.00001210306,0.00003562438,0.0001571018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9633919,"threshold_uncertainty_score":0.601995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02083160643607138,"score_gpt":0.2335808349449987,"score_spread":0.2127492285089273,"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."}}