{"id":"W4408363681","doi":"10.1007/s10846-025-02235-2","title":"Risk-Sensitive Autonomous Exploration of Unknown Environments: A Deep Reinforcement Learning Perspective","year":2025,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Alberta","keywords":"Reinforcement learning; Perspective (graphical); Computer science; Artificial intelligence; Reinforcement; Human–computer interaction; Psychology; Social psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001419644,0.0002782432,0.0006465013,0.0006177764,0.0001752089,0.000177564,0.0007777038,0.0001179863,0.000008625922],"category_scores_gemma":[0.0006740517,0.000244981,0.0002765084,0.0005319497,0.00009310926,0.0009560239,0.0002328943,0.0005951573,0.00003509234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065226,"about_ca_system_score_gemma":0.0002262407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001073835,"about_ca_topic_score_gemma":0.000002311124,"domain_scores_codex":[0.9963783,0.00048256,0.001669773,0.000302283,0.0008269323,0.000340134],"domain_scores_gemma":[0.9958667,0.0004280891,0.002488249,0.0004606991,0.0006364757,0.0001197581],"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":[0.00003014704,0.00006434778,0.0004049832,0.00004744004,0.0003955318,0.0000211614,0.004980858,0.9525413,0.0002911069,0.04020509,0.00007951205,0.0009385065],"study_design_scores_gemma":[0.0004700106,0.0008834855,0.0002271646,0.0006124761,0.0001371419,0.00005498872,0.008361815,0.9826198,0.004713433,0.0003144077,0.001385196,0.0002201197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005675891,0.0008292908,0.9929447,0.0002187863,0.001932638,0.0004166837,1.205859e-7,0.00003115833,0.003059078],"genre_scores_gemma":[0.9893649,0.0005740204,0.006955797,0.00004134274,0.0001257924,0.000008003121,0.000001485792,0.00001633874,0.002912353],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9887973,"threshold_uncertainty_score":0.9990036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01857804833973843,"score_gpt":0.2630788909470452,"score_spread":0.2445008426073068,"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."}}