{"id":"https://openalex.org/W2100476620","doi":"https://doi.org/10.1109/ecc.2015.7331049","title":"Synchronous learning of efficient Nash equilibria in potential games with uncoupled dynamics and memoryless players","display_name":"Synchronous learning of efficient Nash equilibria in potential games with uncoupled dynamics and memoryless players","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W2100476620","doi":"https://doi.org/10.1109/ecc.2015.7331049","mag":"2100476620"},"language":"en","primary_location":{"id":"doi:10.1109/ecc.2015.7331049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ecc.2015.7331049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 European Control Conference (ECC)","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/A5042936048","display_name":"Tatiana Tatarenko","orcid":"https://orcid.org/0000-0001-8951-112X"},"institutions":[{"id":"https://openalex.org/I31512782","display_name":"Technical University of Darmstadt","ror":"https://ror.org/05n911h24","country_code":"DE","type":"education","lineage":["https://openalex.org/I31512782"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Tatiana Tatarenko","raw_affiliation_strings":["Control Methods and Robotics Lab, Darmstadt, Germany","Control Methods and Robotics Lab, TU Darmstadt, 64289, Germany"],"affiliations":[{"raw_affiliation_string":"Control Methods and Robotics Lab, Darmstadt, Germany","institution_ids":["https://openalex.org/I31512782"]},{"raw_affiliation_string":"Control Methods and Robotics Lab, TU Darmstadt, 64289, Germany","institution_ids":["https://openalex.org/I31512782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5042936048"],"corresponding_institution_ids":["https://openalex.org/I31512782"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08713264,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"3334","last_page":"3339"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11031","display_name":"Game Theory and Applications","score":0.9991000294685364,"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"}},"topics":[{"id":"https://openalex.org/T11031","display_name":"Game Theory and Applications","score":0.9991000294685364,"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"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9962999820709229,"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/T10249","display_name":"Distributed Control Multi-Agent Systems","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/potential-game","display_name":"Potential game","score":0.8408722281455994},{"id":"https://openalex.org/keywords/nash-equilibrium","display_name":"Nash equilibrium","score":0.7162377834320068},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6875149011611938},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6085238456726074},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5875992774963379},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5575389862060547},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.547588586807251},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5317403674125671},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4982454776763916},{"id":"https://openalex.org/keywords/fictitious-play","display_name":"Fictitious play","score":0.49709585309028625},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.43963199853897095},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4214862585067749},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3005855679512024},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21272003650665283}],"concepts":[{"id":"https://openalex.org/C2778079155","wikidata":"https://www.wikidata.org/wiki/Q288500","display_name":"Potential game","level":3,"score":0.8408722281455994},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.7162377834320068},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6875149011611938},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6085238456726074},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5875992774963379},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5575389862060547},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.547588586807251},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5317403674125671},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4982454776763916},{"id":"https://openalex.org/C145071142","wikidata":"https://www.wikidata.org/wiki/Q1411116","display_name":"Fictitious play","level":3,"score":0.49709585309028625},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.43963199853897095},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4214862585067749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3005855679512024},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21272003650665283},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ecc.2015.7331049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ecc.2015.7331049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 European Control Conference (ECC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W270482562","https://openalex.org/W750498331","https://openalex.org/W1994908596","https://openalex.org/W2001007182","https://openalex.org/W2024612830","https://openalex.org/W2066767736","https://openalex.org/W2069397923","https://openalex.org/W2088607728","https://openalex.org/W2103151730","https://openalex.org/W2119682789","https://openalex.org/W2168281471","https://openalex.org/W2171234133","https://openalex.org/W2751862591","https://openalex.org/W2753933954","https://openalex.org/W2801154251","https://openalex.org/W3022321359","https://openalex.org/W3123073578","https://openalex.org/W3148958427","https://openalex.org/W6610032579","https://openalex.org/W6656710697","https://openalex.org/W6683640932","https://openalex.org/W6685183561"],"related_works":["https://openalex.org/W4226086326","https://openalex.org/W3175268978","https://openalex.org/W4287266326","https://openalex.org/W3137345903","https://openalex.org/W4210759752","https://openalex.org/W4298859366","https://openalex.org/W2528338391","https://openalex.org/W4214835929","https://openalex.org/W3119014777","https://openalex.org/W3212428133"],"abstract_inverted_index":{"Game":[0],"theoretical":[1],"learning":[2,74,92,133],"in":[3,23,29,99,103],"multi-agent":[4,24,101],"systems":[5,25],"is":[6,54,62,89],"a":[7,58,100,131],"rapidly":[8],"developing":[9],"area":[10],"of":[11,20,31,37,44,51,57,69,120,152],"research.":[12],"It":[13],"gained":[14],"popularity":[15],"since":[16],"the":[17,35,42,55,67,117,149,162],"wide":[18],"range":[19],"optimization":[21],"problems":[22],"can":[26,95],"be":[27,96],"reformulated":[28],"terms":[30],"potential":[32,38,70,136,156],"games,":[33],"where":[34],"set":[36,43,68],"function":[39,71,157],"maximizers":[40],"represents":[41],"optimal":[45,163],"system":[46,80,102,164],"states.":[47],"The":[48],"crucial":[49],"point":[50],"such":[52],"approach":[53],"design":[56],"distributed":[59],"algorithm":[60,93,134,154],"that":[61,94],"guaranteed":[63],"to":[64,66,155,161],"converge":[65],"maximizers.":[72],"Various":[73],"algorithms,":[75],"whose":[76],"features":[77],"depend":[78],"on":[79],"properties,":[81],"have":[82],"been":[83],"proposed":[84],"so":[85],"far.":[86],"However,":[87],"there":[88],"currently":[90],"no":[91],"efficiently":[97],"executed":[98],"which":[104,159],"uncoupled":[105,139],"agents":[106],"update":[107],"their":[108],"actions":[109],"synchronously":[110],"and":[111,141],"do":[112],"not":[113],"take":[114],"into":[115],"account":[116],"past":[118],"history":[119],"actions.":[121],"In":[122],"this":[123,127,153],"paper,":[124],"we":[125],"fill":[126],"gap":[128],"by":[129],"introducing":[130],"new":[132],"for":[135],"games":[137],"with":[138],"dynamics":[140],"memoryless":[142],"players":[143],"who":[144],"act":[145],"synchronously.":[146],"We":[147],"prove":[148],"probabilistic":[150],"convergence":[151],"maximizers,":[158],"correspond":[160],"states":[165],"under":[166],"appropriate":[167],"game":[168],"settings.":[169]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
