{"id":"W4368227480","doi":"10.1109/iccae56788.2023.10111485","title":"Proximity-Based Reward System and Reinforcement Learning for Path Planning","year":2023,"lang":"en","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Reinforcement learning; Motion planning; Computer science; Artificial intelligence; Path (computing); Automation; Machine learning; Robotics; Field (mathematics); Task (project management); Robot; Engineering; Mathematics","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":[0.0007848032,0.0001565821,0.0001788247,0.000190135,0.0003342565,0.0002555063,0.000391902,0.00006321095,0.00000280974],"category_scores_gemma":[0.0001367545,0.0001406385,0.00005367087,0.0003921573,0.00002421297,0.0003124444,0.0002352176,0.0001504524,0.00004837879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006762363,"about_ca_system_score_gemma":0.00006377191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006434552,"about_ca_topic_score_gemma":1.066398e-7,"domain_scores_codex":[0.9985259,0.00004628623,0.0002976193,0.0003641843,0.0003332357,0.0004327569],"domain_scores_gemma":[0.9990776,0.0002701424,0.0001342005,0.0003377475,0.00007876002,0.000101532],"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":[0.000007037293,0.000001870338,0.001609588,0.0002163988,0.00001205248,0.000008538233,0.0003471849,0.9709044,0.00008082686,0.02527423,0.000831257,0.0007065873],"study_design_scores_gemma":[0.0005177206,0.0002483992,0.0003168527,0.0001347565,0.000006528109,0.000003398126,0.0001916765,0.9902287,0.0003646408,0.00003409542,0.00776962,0.0001836812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002059817,0.00001189763,0.9916199,0.0003197775,0.000230562,0.0005074202,1.593407e-7,0.001388395,0.003861998],"genre_scores_gemma":[0.9417554,0.00000264106,0.05329956,0.0001836853,0.00005265156,0.0001047461,0.00001823111,0.00002037508,0.004562756],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9396955,"threshold_uncertainty_score":0.5735071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03200085973623594,"score_gpt":0.2690569178496535,"score_spread":0.2370560581134175,"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."}}