{"id":"W4285045354","doi":"10.20517/ir.2022.11","title":"AVDDPG – Federated reinforcement learning applied to autonomous platoon control","year":2022,"lang":"en","type":"article","venue":"Intelligence & Robotics","topic":"Traffic control and management","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Platoon; Reinforcement learning; Reinforcement; Computer science; Materials science; Control (management); Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001074886,0.0006710607,0.0006907781,0.0003151874,0.0003479992,0.0007674682,0.0009612944,0.0006767671,0.001887277],"category_scores_gemma":[0.001860451,0.0002417741,0.0004167143,0.0002578485,0.0005696643,0.0004796498,0.001043728,0.0009892277,0.0002914015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006287612,"about_ca_system_score_gemma":0.001068558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005897322,"about_ca_topic_score_gemma":0.00313101,"domain_scores_codex":[0.9994771,0.0001410524,0.00003231569,0.000126855,0.0001391012,0.00008368386],"domain_scores_gemma":[0.9993415,0.0002586464,0.00007931788,0.00008477741,0.0001723887,0.00006332059],"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":[0.0001103335,0.00007894426,0.0007824241,0.00007216288,0.00003689226,0.00009243568,0.00004096156,0.933041,0.002545304,0.00297359,0.0007275291,0.05949842],"study_design_scores_gemma":[0.00001077558,0.00005825009,0.00009781949,0.000004616752,0.000003905577,0.00001373277,0.000003531657,0.9977186,0.0006766834,0.0009968444,0.0004118527,0.000003442041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04233232,0.0004260154,0.9496284,0.0002177995,0.0001361305,0.0001260781,0.00006985964,0.002026341,0.005037044],"genre_scores_gemma":[0.9552971,0.0001142515,0.04277203,0.00008657444,0.00002106236,0.00009020278,0.0000662321,0.00003471324,0.001517867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005897322,"threshold_uncertainty_score":0.01172596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024857588274035,"score_gpt":0.2051875602226315,"score_spread":0.1949389843398912,"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."}}