{"id":"W4407666459","doi":"10.1051/itmconf/20257301005","title":"Optimizing Robotic Arm Control Using Deep Deterministic Policy Gradient: An Exploration of Hyperparameter Tuning","year":2025,"lang":"en","type":"article","venue":"ITM Web of Conferences","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Hyperparameter; Robotic arm; Computer science; Control (management); Artificial intelligence","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":[0.002258245,0.001089057,0.001087429,0.0005137796,0.0002977282,0.0009851195,0.00111402,0.001039162,0.0008682652],"category_scores_gemma":[0.007945027,0.0006920854,0.0004536155,0.000352759,0.001069365,0.001211965,0.001006528,0.001574004,0.0001658443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124359,"about_ca_system_score_gemma":0.001720363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007071854,"about_ca_topic_score_gemma":0.005234509,"domain_scores_codex":[0.9993966,0.0002689386,0.00003441006,0.0001038094,0.0001156259,0.00008059445],"domain_scores_gemma":[0.9969823,0.002298486,0.0002194027,0.0001747413,0.000255554,0.00006955492],"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":[0.00003251707,0.00002833583,0.0003431882,0.00003018203,0.00001805997,0.00001700426,0.00002104505,0.9849806,0.0005812498,0.001257196,0.0001598243,0.01253077],"study_design_scores_gemma":[0.000004989236,0.00001793786,0.00003861758,0.000005055578,0.000002904933,0.000003720217,0.000003314397,0.9988768,0.0002438952,0.000723638,0.00007687732,0.000002307214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.113142,0.001741552,0.8791546,0.0007729184,0.0000617829,0.0001073144,0.00003592701,0.0008421924,0.004141759],"genre_scores_gemma":[0.9225785,0.0004137537,0.07555619,0.0001799438,0.00002352244,0.0001299122,0.00003484191,0.00009029016,0.0009930388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007071854,"threshold_uncertainty_score":0.01406139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07324712132754402,"score_gpt":0.3211878497326386,"score_spread":0.2479407284050946,"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."}}