{"id":"W4205522428","doi":"10.1109/icrae53653.2021.9657767","title":"Deep Reinforcement Learning for Flocking Control of UAVs in Complex Environments","year":2021,"lang":"en","type":"article","venue":"","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Flocking (texture); Reinforcement learning; Computer science; Markov decision process; Kinematics; Partially observable Markov decision process; Collision; Swarm behaviour; Artificial intelligence; Distributed computing; Markov process; Markov chain; Machine learning; Markov model; 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":[0.0006880501,0.0006705981,0.000622307,0.0002333158,0.0003380173,0.0004237971,0.0007357958,0.0006805381,0.0008511498],"category_scores_gemma":[0.00171299,0.0003357843,0.0002998584,0.0001718261,0.0006911827,0.00053775,0.0007433603,0.0010487,0.00011366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009319822,"about_ca_system_score_gemma":0.001206413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01173363,"about_ca_topic_score_gemma":0.009548142,"domain_scores_codex":[0.9998179,0.00004415323,0.000008042717,0.00004548326,0.00003837746,0.00004615513],"domain_scores_gemma":[0.9993765,0.0003160065,0.0001018829,0.00003704819,0.0001156802,0.00005293049],"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":[0.00002221358,0.00001987238,0.0004558089,0.00001371674,0.00001004171,0.00002259407,0.00001757755,0.9887106,0.0006064128,0.001301095,0.0001828495,0.00863736],"study_design_scores_gemma":[0.000001641953,0.000004895211,0.00002511026,8.005843e-7,7.942253e-7,9.922526e-7,0.000001016011,0.9995346,0.00006941502,0.0003368134,0.00002346976,5.208638e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1265342,0.0004887199,0.8688517,0.0004322642,0.00006920769,0.00004209902,0.00004267093,0.0005378227,0.003001399],"genre_scores_gemma":[0.981125,0.00006786473,0.01775557,0.00005968601,0.00001044239,0.00003311304,0.00003453663,0.0000177515,0.00089617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01173363,"threshold_uncertainty_score":0.02333063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02372252897729252,"score_gpt":0.2439857517237118,"score_spread":0.2202632227464193,"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."}}