{"id":"W4283026787","doi":"10.1007/s41315-022-00245-z","title":"Proximal policy optimization for formation navigation and obstacle avoidance","year":2022,"lang":"en","type":"article","venue":"International Journal of Intelligent Robotics and Applications","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Centroid; Obstacle avoidance; Computer science; Function (biology); Bearing (navigation); Order (exchange); Obstacle; Control (management); Artificial intelligence; State (computer science); State information; Holonomic; Algorithm; Robot; Mobile robot; Political science; Economics; Law","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":[0.000336255,0.00008316721,0.0001113118,0.000194094,0.0002107726,0.000204374,0.0004836039,0.00002223629,0.000003331217],"category_scores_gemma":[0.00005097854,0.00008420134,0.0000526368,0.0001744253,0.00002473752,0.0004637404,0.000147495,0.0001067641,7.37407e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178773,"about_ca_system_score_gemma":0.0000793876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000726016,"about_ca_topic_score_gemma":4.765528e-7,"domain_scores_codex":[0.9989151,0.00003501505,0.0004641,0.0001395796,0.0003412076,0.0001049431],"domain_scores_gemma":[0.9986639,0.00009894742,0.0004866645,0.0001148154,0.000567221,0.00006844055],"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":[0.00002393818,0.0001273655,0.0001307411,0.00001808276,0.00005781465,0.000001730128,0.0003679678,0.6278437,0.0005292814,0.311193,0.0001826333,0.05952379],"study_design_scores_gemma":[0.0005306371,0.0001186522,0.0001170793,0.00002394333,0.00001506378,0.0002936336,0.0002317981,0.9732931,0.0006446646,0.007577984,0.01704124,0.0001121742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001122827,0.0002720841,0.9934695,0.004368711,0.0002349289,0.0004220612,0.00004681144,0.0000166067,0.00004644367],"genre_scores_gemma":[0.9163004,0.0001267172,0.08277676,0.0001990535,0.0002898729,0.0001787068,0.00006645959,0.000008615485,0.0000534237],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9151776,"threshold_uncertainty_score":0.3433631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01583224782403933,"score_gpt":0.2791423078751722,"score_spread":0.2633100600511328,"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."}}