{"id":"W4394907649","doi":"10.21203/rs.3.rs-4254664/v1","title":"Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Reinforcement learning; Mechanism (biology); Field (mathematics); Robot; Reinforcement; Computer science; Artificial intelligence; Control (management); Engineering; Human–computer interaction; Physics; Structural 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004529803,0.0003992395,0.0004378809,0.0002650574,0.0002785502,0.000335456,0.0008990553,0.00045545,0.001471822],"category_scores_gemma":[0.0007531084,0.0002198355,0.000274778,0.000130929,0.0006420427,0.0004854126,0.0006252126,0.0005576624,0.0001981011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005808264,"about_ca_system_score_gemma":0.0006956351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004337633,"about_ca_topic_score_gemma":0.003456613,"domain_scores_codex":[0.999851,0.00002809042,0.000006339551,0.0000426688,0.0000418061,0.00003009719],"domain_scores_gemma":[0.9996131,0.0001181814,0.00007749903,0.00005215185,0.00009669906,0.00004253734],"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":[0.00008177031,0.0000759489,0.001060101,0.00003204713,0.00002791141,0.00008816161,0.0000519513,0.9294042,0.00858942,0.006285158,0.0007843316,0.05351891],"study_design_scores_gemma":[0.000004595771,0.00002134915,0.00006858962,0.000001329987,0.000001877221,0.000007489125,0.000001896685,0.9984093,0.0005238274,0.0008161358,0.0001418311,0.000001896433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09437632,0.0001724953,0.9002575,0.0001856875,0.00004461955,0.00004557586,0.00002535005,0.001200141,0.003692252],"genre_scores_gemma":[0.9659936,0.00003970225,0.03173015,0.00004386859,0.000008223772,0.00003943324,0.00002083389,0.00002118492,0.002103051],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004337633,"threshold_uncertainty_score":0.008624792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03066027142451939,"score_gpt":0.3467320612982002,"score_spread":0.3160717898736808,"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."}}