{"id":"W3197466741","doi":"10.48550/arxiv.2109.03331","title":"CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Training (meteorology); Computer science; Cyber-physical system; Computer network; Computer security; Operating system; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004548551,0.0004852561,0.0006341176,0.0002069193,0.0003124537,0.0006252154,0.002036397,0.0005003658,0.0000423942],"category_scores_gemma":[0.00007177515,0.0006036844,0.0004007182,0.0006982058,0.00006931671,0.0004517735,0.002113828,0.0007147585,0.00003255115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004709896,"about_ca_system_score_gemma":0.0004624281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001306627,"about_ca_topic_score_gemma":0.00001087355,"domain_scores_codex":[0.9969318,0.0002045651,0.0004374279,0.001425822,0.0001836036,0.0008167882],"domain_scores_gemma":[0.9971024,0.0002461379,0.0005694251,0.001568988,0.0002748834,0.0002381331],"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":[0.00001265947,0.00002395889,0.0008482317,0.0001212874,0.0002831376,0.0002680046,0.0007254291,0.9682512,0.000004437926,0.02852716,0.0007955711,0.000138978],"study_design_scores_gemma":[0.0007023285,0.00005778832,0.0008282007,0.0003237893,0.0001178269,0.000007471836,0.0001327806,0.9930261,0.000004113962,0.0005910491,0.003592085,0.0006164918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04979448,0.00009176245,0.9431168,0.00002731338,0.003115953,0.0007992492,0.000007047859,0.0004769436,0.002570397],"genre_scores_gemma":[0.98625,0.00004811915,0.006037511,0.000141718,0.0002676426,0.000005405829,0.00008646094,0.00005216691,0.007110971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9370794,"threshold_uncertainty_score":0.9996415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1234854113982818,"score_gpt":0.2151239970408133,"score_spread":0.09163858564253148,"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."}}