{"id":"https://openalex.org/W4417025149","doi":"https://doi.org/10.48550/arxiv.2512.03166","title":"Multi-Agent Reinforcement Learning and Real-Time Decision-Making in Robotic Soccer for Virtual Environments","display_name":"Multi-Agent Reinforcement Learning and Real-Time Decision-Making in Robotic Soccer for Virtual Environments","publication_year":2025,"publication_date":"2025-12-02","ids":{"openalex":"https://openalex.org/W4417025149","doi":"https://doi.org/10.48550/arxiv.2512.03166"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.03166","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.03166","pdf_url":"https://arxiv.org/pdf/2512.03166","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.03166","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115734057","display_name":"Aya Taourirte","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Taourirte, Aya","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5018681890","display_name":"Md Sohag Mia","orcid":"https://orcid.org/0009-0002-4096-8596"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mia, Md Sohag","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5115734057"],"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9186999797821045,"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":0.9186999797821045,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.008700000122189522,"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/T12794","display_name":"Adaptive Dynamic Programming Control","score":0.006399999838322401,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8621000051498413},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5637999773025513},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5435000061988831},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.45010000467300415},{"id":"https://openalex.org/keywords/emulation","display_name":"Emulation","score":0.4357999861240387},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.40959998965263367},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.3384999930858612},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.33239999413490295}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8621000051498413},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7293000221252441},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5637999773025513},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5435000061988831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5285999774932861},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.45010000467300415},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.4357999861240387},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34290000796318054},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3343999981880188},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C25344961","wikidata":"https://www.wikidata.org/wiki/Q192726","display_name":"Virtual machine","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C188888258","wikidata":"https://www.wikidata.org/wiki/Q7353390","display_name":"Robot learning","level":4,"score":0.3181999921798706},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.31349998712539673},{"id":"https://openalex.org/C199190896","wikidata":"https://www.wikidata.org/wiki/Q3509276","display_name":"Learning classifier system","level":3,"score":0.30649998784065247},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2976999878883362},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.28139999508857727},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.2694000005722046}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.03166","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.03166","pdf_url":"https://arxiv.org/pdf/2512.03166","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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.2512.03166","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.03166","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2512.03166","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.03166","pdf_url":"https://arxiv.org/pdf/2512.03166","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"deployment":[1],"of":[2,24,41,51,188],"multi-agent":[3,209],"systems":[4],"in":[5,191,207],"dynamic,":[6],"adversarial":[7],"environments":[8],"like":[9],"robotic":[10],"soccer":[11],"necessitates":[12],"real-time":[13,84],"decision-making,":[14],"sophisticated":[15],"cooperation,":[16],"and":[17,48,127,182,204],"scalable":[18],"algorithms":[19],"to":[20,111,139,142],"avoid":[21],"the":[22,39,49,108,113,150,161,192],"curse":[23],"dimensionality.":[25],"While":[26],"Reinforcement":[27,61],"Learning":[28,62],"(RL)":[29],"offers":[30],"a":[31,58,72,80,101,116,123,128,157,169],"promising":[32],"framework,":[33,152],"existing":[34],"methods":[35],"often":[36],"struggle":[37],"with":[38,87],"multi-granularity":[40],"tasks":[42],"(long-term":[43],"strategy":[44,135],"vs.":[45,160],"instant":[46],"actions)":[47],"complexity":[50],"large-scale":[52],"agent":[53,159],"interactions.":[54],"This":[55],"paper":[56],"presents":[57],"unified":[59],"Multi-Agent":[60],"(MARL)":[63],"framework":[64,110],"that":[65],"addresses":[66],"these":[67],"challenges.":[68],"First,":[69],"we":[70,99,145],"establish":[71],"baseline":[73],"using":[74],"Proximal":[75],"Policy":[76],"Optimization":[77],"(PPO)":[78],"within":[79],"client-server":[81],"architecture":[82],"for":[83,201],"action":[85,130],"scheduling,":[86],"PPO":[88],"demonstrating":[89,198],"superior":[90],"performance":[91,171],"(4.32":[92],"avg.":[93,174],"goals,":[94,175],"82.9%":[95],"ball":[96,177],"control).":[97],"Second,":[98],"introduce":[100],"Hierarchical":[102],"RL":[103],"(HRL)":[104],"structure":[105],"based":[106],"on":[107],"options":[109],"decompose":[112],"problem":[114],"into":[115,149,156],"high-level":[117],"trajectory":[118],"planning":[119],"layer":[120],"(modeled":[121],"as":[122],"Semi-Markov":[124],"Decision":[125],"Process)":[126],"low-level":[129],"execution":[131],"layer,":[132],"improving":[133],"global":[134],"(avg.":[136],"goals":[137],"increased":[138],"5.26).":[140],"Finally,":[141],"ensure":[143],"scalability,":[144],"integrate":[146],"mean-field":[147,165],"theory":[148],"HRL":[151],"simplifying":[153],"many-agent":[154],"interactions":[155],"single":[158],"population":[162],"average.":[163],"Our":[164],"actor-critic":[166],"method":[167],"achieves":[168],"significant":[170],"boost":[172],"(5.93":[173],"89.1%":[176],"control,":[178],"92.3%":[179],"passing":[180],"accuracy)":[181],"enhanced":[183],"training":[184],"stability.":[185],"Extensive":[186],"simulations":[187],"4v4":[189],"matches":[190],"Webots":[193],"environment":[194],"validate":[195],"our":[196],"approach,":[197],"its":[199],"potential":[200],"robust,":[202],"scalable,":[203],"cooperative":[205],"behavior":[206],"complex":[208],"domains.":[210]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-05T00:00:00"}
