{"id":"https://openalex.org/W7161286418","doi":"https://doi.org/10.48550/arxiv.2605.14379","title":"Data-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Games","display_name":"Data-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Games","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161286418","doi":"https://doi.org/10.48550/arxiv.2605.14379"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14379","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.14379","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062758397","display_name":"JB Lanier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lanier, JB","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119938384","display_name":"Nathan Monette","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Monette, Nathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103265896","display_name":"Pierre Baldi","orcid":"https://orcid.org/0000-0003-0636-7930"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baldi, Pierre","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073588383","display_name":"Roy Fox","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fox, Roy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.7376000285148621,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.7376000285148621,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.1543000042438507,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.02239999920129776,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5697000026702881},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.553600013256073},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5264000296592712},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5115000009536743},{"id":"https://openalex.org/keywords/complete-information","display_name":"Complete information","score":0.5056999921798706},{"id":"https://openalex.org/keywords/counterexample","display_name":"Counterexample","score":0.5016999840736389},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4528999924659729},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.45019999146461487},{"id":"https://openalex.org/keywords/game-theory","display_name":"Game theory","score":0.4251999855041504},{"id":"https://openalex.org/keywords/nash-equilibrium","display_name":"Nash equilibrium","score":0.41690000891685486}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7355999946594238},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5697000026702881},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.553600013256073},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5264000296592712},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5115000009536743},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C162838799","wikidata":"https://www.wikidata.org/wiki/Q596077","display_name":"Counterexample","level":2,"score":0.5016999840736389},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4528999924659729},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.45019999146461487},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C123676819","wikidata":"https://www.wikidata.org/wiki/Q1074338","display_name":"Perfect information","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C202556891","wikidata":"https://www.wikidata.org/wiki/Q1584646","display_name":"Repeated game","level":3,"score":0.3871000111103058},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36399999260902405},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.36090001463890076},{"id":"https://openalex.org/C73795354","wikidata":"https://www.wikidata.org/wiki/Q287618","display_name":"Sequential game","level":3,"score":0.35749998688697815},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3508000075817108},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.34369999170303345},{"id":"https://openalex.org/C88959737","wikidata":"https://www.wikidata.org/wiki/Q1546627","display_name":"Strategy","level":3,"score":0.3431999981403351},{"id":"https://openalex.org/C145071142","wikidata":"https://www.wikidata.org/wiki/Q1411116","display_name":"Fictitious play","level":3,"score":0.33709999918937683},{"id":"https://openalex.org/C146930158","wikidata":"https://www.wikidata.org/wiki/Q656416","display_name":"Extensive-form game","level":4,"score":0.31859999895095825},{"id":"https://openalex.org/C200594392","wikidata":"https://www.wikidata.org/wiki/Q812529","display_name":"Bayesian game","level":4,"score":0.3133000135421753},{"id":"https://openalex.org/C2778079155","wikidata":"https://www.wikidata.org/wiki/Q288500","display_name":"Potential game","level":3,"score":0.30469998717308044},{"id":"https://openalex.org/C164407509","wikidata":"https://www.wikidata.org/wiki/Q5384490","display_name":"Equilibrium selection","level":4,"score":0.30219998955726624},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C3018412434","wikidata":"https://www.wikidata.org/wiki/Q7889","display_name":"Video game","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C201364048","wikidata":"https://www.wikidata.org/wiki/Q2897085","display_name":"Sequential equilibrium","level":5,"score":0.28610000014305115},{"id":"https://openalex.org/C73602740","wikidata":"https://www.wikidata.org/wiki/Q7795822","display_name":"Thompson sampling","level":3,"score":0.2851000130176544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2759000062942505},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C32407928","wikidata":"https://www.wikidata.org/wiki/Q2733833","display_name":"Best response","level":3,"score":0.271699994802475},{"id":"https://openalex.org/C196921405","wikidata":"https://www.wikidata.org/wiki/Q786431","display_name":"Online algorithm","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C22171661","wikidata":"https://www.wikidata.org/wiki/Q1074380","display_name":"Stochastic game","level":2,"score":0.265500009059906}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14379","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.14379","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14379","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Finding":[0],"approximate":[1],"equilibria":[2],"for":[3,48],"large-scale":[4],"imperfect-information":[5],"competitive":[6],"games":[7,150,167,208],"such":[8],"as":[9,101],"StarCraft,":[10],"Dota,":[11],"and":[12,21,112,123,173,202],"CounterStrike":[13],"remains":[14],"computationally":[15],"infeasible":[16],"due":[17],"to":[18,38,71,90,98,128,145,170,179],"sparse":[19],"rewards":[20],"challenging":[22,154],"exploration":[23,42,92,200],"over":[24,132],"long":[25],"horizons.":[26],"In":[27],"this":[28,99,180],"paper,":[29],"we":[30,74,106,174,189],"propose":[31,75],"a":[32,124,176,191],"multi-agent":[33],"starting-state":[34],"sampling":[35],"strategy":[36],"designed":[37,127],"substantially":[39],"accelerate":[40],"online":[41],"in":[43,149,181,205],"regularized":[44,141],"policy-gradient":[45],"game":[46,126],"methods":[47,144],"two-player":[49,119],"zero-sum":[50],"(2p0s)":[51],"games.":[52],"Motivated":[53],"by":[54],"an":[55],"assumption":[56],"that":[57,158,197],"offline":[58,88],"demonstrations":[59],"from":[60,87],"skilled":[61],"humans":[62],"can":[63,168],"provide":[64,175],"good":[65],"coverage":[66],"of":[67,78,93,118,184,194],"high-level":[68],"strategies":[69],"relevant":[70,95],"equilibrium":[72],"play,":[73],"the":[76,182],"initialization":[77],"reinforcement":[79],"learning":[80],"data":[81,89],"collection":[82],"at":[83],"intermediate":[84],"states":[85],"sampled":[86],"facilitate":[91],"strategically":[94],"subgames.":[96],"Referring":[97],"method":[100],"Data-Augmented":[102],"Game":[103],"Starts":[104],"(DAGS),":[105],"perform":[107],"experiments":[108],"using":[109],"synthetic":[110],"datasets":[111],"analytically":[113,213],"tractable,":[114],"long-horizon":[115],"control":[116],"variants":[117],"Kuhn":[120],"Poker,":[121],"Goofspiel,":[122],"counterexample":[125],"penalize":[129],"biased":[130,171],"beliefs":[131],"hidden":[133],"information.":[134],"Under":[135],"fixed":[136],"computational":[137],"budgets,":[138],"DAGS":[139],"enables":[140],"policy":[142],"gradient":[143],"achieve":[146],"lower":[147],"exploitability":[148,211],"with":[151],"significantly":[152],"more":[153],"exploration.":[155],"We":[156],"show":[157],"augmenting":[159],"starting":[160],"state":[161,203],"distributions":[162],"when":[163],"solving":[164],"imperfect":[165],"information":[166],"lead":[169],"equilibria,":[172],"straightforward":[177],"mitigation":[178],"form":[183],"multi-task":[185],"observation":[186],"flags.":[187],"Finally,":[188],"release":[190],"new":[192],"set":[193],"benchmark":[195],"environments":[196],"drastically":[198],"increase":[199],"challenges":[201],"counts":[204],"existing":[206],"OpenSpiel":[207],"while":[209],"keeping":[210],"measurements":[212],"tractable.":[214]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}