{"id":"W3206465908","doi":"10.1155/2021/7765130","title":"An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Liaoning Revitalization Talents Program","keywords":"Reinforcement learning; Path (computing); Convergence (economics); Motion planning; Computer science; Mathematical optimization; Artificial neural network; Function (biology); Plan (archaeology); State (computer science); Action (physics); Artificial intelligence; Algorithm; Mathematics; Robot","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003232207,0.0009136426,0.0008105762,0.0004734947,0.0004189053,0.000619779,0.001090814,0.0008708361,0.002737951],"category_scores_gemma":[0.0009263213,0.0005474683,0.0006889767,0.0004724654,0.0004078988,0.0008511822,0.0009000083,0.001076691,0.0003707722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007642188,"about_ca_system_score_gemma":0.001937852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01386618,"about_ca_topic_score_gemma":0.009480831,"domain_scores_codex":[0.999781,0.00002934798,0.00001566059,0.00007726665,0.00005541306,0.00004140095],"domain_scores_gemma":[0.9997625,0.0000822804,0.00002945753,0.00001875451,0.00008414531,0.00002293288],"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":[0.00005478676,0.00002395863,0.0006687728,0.00009945493,0.00003176755,0.0001502377,0.00007907099,0.8822724,0.00330394,0.006596989,0.001871783,0.1048468],"study_design_scores_gemma":[0.00000931221,0.00001534759,0.00005892714,0.000003663417,0.0000053634,0.00002033078,0.000006503649,0.997839,0.0004296031,0.001041274,0.0005669869,0.000003631135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008966317,0.0002290692,0.9880325,0.0001363116,0.00005288384,0.00003560877,0.00004364101,0.000441745,0.002061955],"genre_scores_gemma":[0.5704724,0.0004009116,0.4201962,0.0002125648,0.0000508522,0.0002620174,0.0002742655,0.0001986491,0.007932235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01386618,"threshold_uncertainty_score":0.02757096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035920956518726,"score_gpt":0.2800826280220552,"score_spread":0.2697234184568679,"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."}}