{"id":"W4392910934","doi":"10.32920/25412560","title":"Deep Reinforcement Learning Controller Design for Unmanned Aerial Vehicles","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"PID controller; Control theory (sociology); Reinforcement learning; Controller (irrigation); Trajectory; Computer science; Linearization; Path (computing); Track (disk drive); Drone; Tracking (education); Control engineering; Artificial intelligence; Engineering; Control (management); Nonlinear system; Physics","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":[0.0007137522,0.0006055532,0.0004705812,0.0002186273,0.0002203814,0.0006325305,0.0007470025,0.000717853,0.001779064],"category_scores_gemma":[0.001488457,0.0003657767,0.0002412731,0.0001550796,0.0004750662,0.0003470627,0.0006612978,0.001011511,0.0003146743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006889238,"about_ca_system_score_gemma":0.0009327204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006972771,"about_ca_topic_score_gemma":0.005677464,"domain_scores_codex":[0.999826,0.00003292117,0.000008784352,0.00004075564,0.00005834502,0.00003306147],"domain_scores_gemma":[0.9995422,0.0001719697,0.00006636805,0.00002524364,0.0001636486,0.00003063791],"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":[0.00003100147,0.00002237947,0.000217677,0.00004414647,0.0000186385,0.00003607858,0.00002990062,0.9685574,0.002372558,0.003123445,0.0005820076,0.02496483],"study_design_scores_gemma":[0.000004136748,0.00001442956,0.00002288802,0.000002384609,0.000001262751,0.000001963046,0.000001438381,0.9990094,0.0002202792,0.0005385996,0.0001823263,9.877818e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01954146,0.0003753853,0.9755275,0.0002244773,0.00008034179,0.00005417054,0.00003311451,0.0005306805,0.003632836],"genre_scores_gemma":[0.9248258,0.0001882974,0.06892926,0.0001348128,0.00003674523,0.0001888563,0.0000700715,0.00005436946,0.005571835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006972771,"threshold_uncertainty_score":0.01386434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02544719689650084,"score_gpt":0.265665793396314,"score_spread":0.2402185964998132,"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."}}