{"id":"https://openalex.org/W7204455308","doi":"https://doi.org/10.48550/arxiv.2608.25419","title":"BVR Sim: An Open and High-Throughput Environment for Heterogeneous Air-Combat Reinforcement Learning","display_name":"BVR Sim: An Open and High-Throughput Environment for Heterogeneous Air-Combat Reinforcement Learning","publication_year":2026,"publication_date":"2026-08-26","ids":{"openalex":"https://openalex.org/W7204455308","doi":"https://doi.org/10.48550/arxiv.2608.25419"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.25419","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.25419","pdf_url":"https://arxiv.org/pdf/2608.25419","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.25419","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103877189","display_name":"Haocheng Sun","orcid":"https://orcid.org/0009-0006-8146-7527","observed_orcids":["https://orcid.org/0009-0006-8146-7527"]},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sun, Haocheng","raw_affiliation_strings":["Beijing University of Posts,Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts,Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023991290","display_name":"Mulai Tan","orcid":"https://orcid.org/0000-0001-5009-6301","observed_orcids":["https://orcid.org/0000-0001-5009-6301"]},"institutions":[{"id":"https://openalex.org/I4210104252","display_name":"Air Force Engineering University","ror":"https://ror.org/00seraz22","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210104252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tan, Mulai","raw_affiliation_strings":["Air Force Engineering University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Air Force Engineering University","institution_ids":["https://openalex.org/I4210104252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9804999828338623,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9804999828338623,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12158","display_name":"Guidance and Control Systems","score":0.004600000102072954,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13855","display_name":"Aerospace Engineering and Control Systems","score":0.002300000051036477,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/beyond-visual-range-air-combat","display_name":"beyond visual range air combat","score":0.9950000047683716},{"id":"https://openalex.org/keywords/reinforcement-learning-environment","display_name":"reinforcement learning environment","score":0.7680000066757202},{"id":"https://openalex.org/keywords/multi-agent-reinforcement-learning","display_name":"multi-agent reinforcement learning","score":0.7799999713897705},{"id":"https://openalex.org/keywords/aircraft-energy-management","display_name":"aircraft energy management","score":0.546999990940094},{"id":"https://openalex.org/keywords/high-throughput-simulation","display_name":"high-throughput simulation","score":0.671999990940094},{"id":"https://openalex.org/keywords/f-16","display_name":"F-16","score":0.5899999737739563},{"id":"https://openalex.org/keywords/f-a-18","display_name":"F/A-18","score":0.8799999952316284},{"id":"https://openalex.org/keywords/f-22","display_name":"F-22","score":0.8629999756813049},{"id":"https://openalex.org/keywords/f-15","display_name":"F-15","score":0.8130000233650208}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7142999768257141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5595999956130981},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5525000095367432},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3174000084400177},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.29679998755455017},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2921000123023987},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2867000102996826},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2736000120639801},{"id":"https://openalex.org/C2778648169","wikidata":"https://www.wikidata.org/wiki/Q967768","display_name":"Compatibility (geochemistry)","level":2,"score":0.2676999866962433}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.25419","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.25419","pdf_url":"https://arxiv.org/pdf/2608.25419","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.25419","doi":"https://doi.org/10.48550/arxiv.2608.25419","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.25419","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.25419","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.25419","pdf_url":"https://arxiv.org/pdf/2608.25419","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204455308.pdf","grobid_xml":"https://content.openalex.org/works/W7204455308.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W4393248251","https://openalex.org/W4415311654"],"related_works":["https://openalex.org/W4393248251","https://openalex.org/W7126052899","https://openalex.org/W7223550794","https://openalex.org/W4385399055","https://openalex.org/W3089592508","https://openalex.org/W4379415663","https://openalex.org/W7171123729","https://openalex.org/W7160413281","https://openalex.org/W4409494016","https://openalex.org/W7221444399"],"abstract_inverted_index":{"Beyond-visual-range":[0],"(BVR)":[1],"air":[2],"combat":[3],"is":[4],"a":[5,105,142],"challenging":[6],"reinforcement-learning":[7,162],"domain":[8],"characterized":[9],"by":[10],"partial":[11],"observability,":[12],"long-horizon":[13],"decision":[14,107],"making,":[15],"energy":[16],"management,":[17],"and":[18,47,54,66,85,96,98,121,152],"limited":[19],"weapons.":[20],"We":[21],"present":[22],"BVR":[23,35],"Sim,":[24],"an":[25],"open-source":[26],"Gymnasium-style":[27],"environment":[28,81],"designed":[29],"for":[30,100],"heterogeneous":[31,78],"air-combat":[32],"reinforcement":[33],"learning.":[34],"Sim":[36],"supports":[37],"multiple":[38],"JSBSim":[39],"aircraft":[40],"models,":[41],"including":[42],"the":[43,109,132],"F-15,":[44],"F-16,":[45],"F/A-18,":[46],"F-22,":[48],"with":[49,147,159],"configurable":[50],"weapons,":[51],"sensors,":[52],"controllers,":[53,72],"opponents.":[55],"A":[56,127],"unified":[57],"tactical":[58],"action":[59],"interface":[60],"specifies":[61],"desired":[62],"heading,":[63],"altitude,":[64],"speed,":[65],"weapon":[67],"release":[68],"above":[69],"aircraft-specific":[70,148],"inner-loop":[71],"enabling":[73],"policies":[74],"to":[75,137],"operate":[76],"across":[77],"platforms.":[79],"The":[80],"provides":[82],"interchangeable":[83],"Python":[84],"accelerated":[86],"C++":[87,110],"backends,":[88],"entity-oriented":[89],"observations,":[90],"compositional":[91],"rewards,":[92],"scripted":[93],"opponents,":[94],"replay":[95],"visualization,":[97],"adapters":[99],"multi-agent":[101,161],"learning":[102],"frameworks.":[103],"At":[104],"0.4-s":[106],"interval,":[108],"backend":[111],"achieves":[112],"104":[113],"simulated":[114],"seconds":[115],"per":[116],"wall-clock":[117],"second":[118],"in":[119],"1-vs-1":[120],"remains":[122],"practical":[123],"through":[124],"10-vs-10":[125],"scenarios.":[126],"policy":[128],"trained":[129],"only":[130],"on":[131],"F-16":[133],"transfers":[134],"without":[135],"retraining":[136],"four":[138],"unseen":[139],"aircraft,":[140],"reaching":[141],"45.5%":[143],"mean":[144],"win":[145],"rate":[146],"controller":[149],"adaptation.":[150],"MAPPO":[151],"HAPPO":[153],"experiments":[154],"further":[155],"verify":[156],"end-to-end":[157],"compatibility":[158],"standard":[160],"pipelines.":[163]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-28T00:00:00"}