{"id":"https://openalex.org/W4205504069","doi":"https://doi.org/10.1109/cog52621.2021.9619076","title":"Gym-\u00b5RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning","display_name":"Gym-\u00b5RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning","publication_year":2021,"publication_date":"2021-08-17","ids":{"openalex":"https://openalex.org/W4205504069","doi":"https://doi.org/10.1109/cog52621.2021.9619076"},"language":"en","primary_location":{"id":"doi:10.1109/cog52621.2021.9619076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cog52621.2021.9619076","pdf_url":null,"source":{"id":"https://openalex.org/S4363608335","display_name":"2021 IEEE Conference on Games (CoG)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Conference on Games (CoG)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101442842","display_name":"Shengyi Huang","orcid":"https://orcid.org/0000-0003-4986-1365"},"institutions":[{"id":"https://openalex.org/I72816309","display_name":"Drexel University","ror":"https://ror.org/04bdffz58","country_code":"US","type":"education","lineage":["https://openalex.org/I72816309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shengyi Huang","raw_affiliation_strings":["Drexel University,Philadelphia,PA,USA","Drexel University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Drexel University,Philadelphia,PA,USA","institution_ids":["https://openalex.org/I72816309"]},{"raw_affiliation_string":"Drexel University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I72816309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038686010","display_name":"Santiago Onta\u00f1\u00f3n","orcid":"https://orcid.org/0000-0002-9616-2981"},"institutions":[{"id":"https://openalex.org/I72816309","display_name":"Drexel University","ror":"https://ror.org/04bdffz58","country_code":"US","type":"education","lineage":["https://openalex.org/I72816309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Santiago Ontanon","raw_affiliation_strings":["Drexel University,Philadelphia,PA,USA","Drexel University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Drexel University,Philadelphia,PA,USA","institution_ids":["https://openalex.org/I72816309"]},{"raw_affiliation_string":"Drexel University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I72816309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012897368","display_name":"Chris Bamford","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chris Bamford","raw_affiliation_strings":["Queen Mary University,London,United Kindom","Queen Mary University, London, United Kindom"],"affiliations":[{"raw_affiliation_string":"Queen Mary University,London,United Kindom","institution_ids":["https://openalex.org/I166337079"]},{"raw_affiliation_string":"Queen Mary University, London, United Kindom","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080702166","display_name":"Lukasz Grela","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lukasz Grela","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101442842"],"corresponding_institution_ids":["https://openalex.org/I72816309"],"apc_list":null,"apc_paid":null,"fwci":2.0105,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.89689662,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9994999766349792,"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":0.9994999766349792,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.9994999766349792,"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/T11197","display_name":"Digital Games and Media","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8848932981491089},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.746132493019104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45672494173049927},{"id":"https://openalex.org/keywords/video-game","display_name":"Video game","score":0.4449140429496765},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4209159314632416},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.3744647204875946},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.28886187076568604},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.06445324420928955}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8848932981491089},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746132493019104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45672494173049927},{"id":"https://openalex.org/C3018412434","wikidata":"https://www.wikidata.org/wiki/Q7889","display_name":"Video game","level":2,"score":0.4449140429496765},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4209159314632416},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3744647204875946},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.28886187076568604},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.06445324420928955}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cog52621.2021.9619076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cog52621.2021.9619076","pdf_url":null,"source":{"id":"https://openalex.org/S4363608335","display_name":"2021 IEEE Conference on Games (CoG)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Conference on Games (CoG)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320311061","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W149149418","https://openalex.org/W176827212","https://openalex.org/W188922443","https://openalex.org/W1191599655","https://openalex.org/W1522301498","https://openalex.org/W1750339092","https://openalex.org/W1968813584","https://openalex.org/W1978829838","https://openalex.org/W2150823748","https://openalex.org/W2302255633","https://openalex.org/W2602425879","https://openalex.org/W2730357202","https://openalex.org/W2736601468","https://openalex.org/W2749807327","https://openalex.org/W2804452021","https://openalex.org/W2887122272","https://openalex.org/W2946025546","https://openalex.org/W2962937819","https://openalex.org/W2982316857","https://openalex.org/W2995894173","https://openalex.org/W2996037775","https://openalex.org/W3014728935","https://openalex.org/W3037164854","https://openalex.org/W3110282614","https://openalex.org/W3208470237","https://openalex.org/W4288594419","https://openalex.org/W4297797010","https://openalex.org/W4298857966","https://openalex.org/W4299335686","https://openalex.org/W4312309732","https://openalex.org/W6606069932","https://openalex.org/W6607116948","https://openalex.org/W6607633681","https://openalex.org/W6627932998","https://openalex.org/W6631190155","https://openalex.org/W6637770179","https://openalex.org/W6637967152","https://openalex.org/W6740640171","https://openalex.org/W6741002519","https://openalex.org/W6743756900","https://openalex.org/W6748638692","https://openalex.org/W6756231095","https://openalex.org/W6758846586","https://openalex.org/W6762373827","https://openalex.org/W6772005887","https://openalex.org/W6772196467","https://openalex.org/W6775892990","https://openalex.org/W6786098777"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W3084456289","https://openalex.org/W2024136090","https://openalex.org/W4391331176","https://openalex.org/W2031695474","https://openalex.org/W2586732548","https://openalex.org/W3049728571"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"researchers":[3],"have":[4,39],"achieved":[5],"great":[6],"success":[7],"in":[8,29,122,127],"applying":[9],"Deep":[10],"Reinforcement":[11],"Learning":[12],"(DRL)":[13],"algorithms":[14],"to":[15,35,61,88,91,100],"Real-time":[16],"Strategy":[17],"(RTS)":[18],"games,":[19],"creating":[20],"strong":[21],"autonomous":[22],"agents":[23],"that":[24],"could":[25],"defeat":[26,109],"professional":[27],"players":[28],"StarCraft":[30],"II.":[31],"However,":[32],"existing":[33],"approaches":[34],"tackle":[36],"full":[37],"games":[38],"high":[40],"computational":[41],"costs,":[42],"usually":[43],"requiring":[44],"the":[45,116],"use":[46],"of":[47,49,86,138],"thousands":[48],"GPUs":[50],"and":[51,80],"CPUs":[52],"for":[53,76],"weeks.":[54],"This":[55],"paper":[56],"has":[57],"two":[58],"main":[59],"contributions":[60],"address":[62],"this":[63],"issue:":[64],"1)":[65],"We":[66],"introduce":[67],"Gym-JLRTS":[68],"(pronounced":[69],"\u201cgym-micro-RTS\u201d)":[70],"as":[71,95,97],"a":[72,84,123,128,141],"fast-to-run":[73],"RL":[74],"environment":[75],"full-game":[77,93],"RTS":[78],"research":[79],"2)":[81],"we":[82,113],"present":[83],"collection":[85],"techniques":[87],"scale":[89],"DRL":[90,130],"play":[92],"\u00b5RTS":[94,111,118],"well":[96],"ablation":[98],"studies":[99],"demonstrate":[101],"their":[102],"empirical":[103],"importance.":[104],"Our":[105],"best-trained":[106],"bot":[107,112],"can":[108],"every":[110],"tested":[114],"from":[115],"past":[117],"competitions":[119],"when":[120],"working":[121],"single-map":[124],"setting,":[125],"resulting":[126],"state-of-the-art":[129],"agent":[131],"while":[132],"only":[133],"taking":[134],"about":[135],"60":[136],"hours":[137],"training":[139],"using":[140],"single":[142],"machine":[143],"(one":[144],"GPU,":[145],"three":[146],"vCPU.":[147],"16GB":[148],"RAM).":[149]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":9}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}