{"id":"W2899026301","doi":"10.1109/access.2018.2878894","title":"Distributed Reinforcement Learning in Emergency Response Simulation","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reinforcement learning; Computer science; Convergence (economics); Scheme (mathematics); Interdependence; Process (computing); Curse of dimensionality; Artificial intelligence; Emergency response; Distributed computing; Machine learning","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":[0.0002778037,0.00009129263,0.0001080689,0.0001325197,0.00007640969,0.00003519388,0.0001722862,0.00005842852,0.0004756697],"category_scores_gemma":[0.0001266469,0.00008975402,0.00003705857,0.0005554991,0.00002557019,0.0003520528,0.00001943378,0.0001393132,0.00003751556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000895352,"about_ca_system_score_gemma":0.0000111195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004118885,"about_ca_topic_score_gemma":0.00008199395,"domain_scores_codex":[0.9992334,0.00005512714,0.0002582738,0.000125246,0.000131313,0.0001966548],"domain_scores_gemma":[0.9996632,0.00005304387,0.00002920213,0.0001614643,0.00005826564,0.00003480822],"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":[0.00005920137,0.000003447004,0.01459717,0.00001218306,0.000009578082,0.00000141376,0.00015618,0.9810449,0.002240702,0.000005335241,0.0001257299,0.001744173],"study_design_scores_gemma":[0.0001273724,0.00002896022,0.05727103,0.00001008589,0.000007677899,2.506422e-7,0.00002732626,0.9329753,0.007878194,0.0001943713,0.001358236,0.0001211695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7789975,0.0000125066,0.2201381,0.00002487216,0.0002262701,0.00006206962,0.000001232738,0.00009437951,0.0004430603],"genre_scores_gemma":[0.9997475,0.0000149171,0.00002386793,0.000009810195,0.0001162711,0.00000774691,0.00001163195,0.000008997843,0.0000593066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2207499,"threshold_uncertainty_score":0.520825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01676639656093099,"score_gpt":0.310017595011586,"score_spread":0.293251198450655,"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."}}