{"id":"https://openalex.org/W7129992914","doi":"https://doi.org/10.48550/arxiv.2602.14559","title":"Fluid-Agent Reinforcement Learning","display_name":"Fluid-Agent Reinforcement Learning","publication_year":2026,"publication_date":"2026-02-16","ids":{"openalex":"https://openalex.org/W7129992914","doi":"https://doi.org/10.48550/arxiv.2602.14559"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.14559","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.14559","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.14559","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126197663","display_name":"Shishir Sharma","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sharma, Shishir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126187371","display_name":"Doina Precup","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Precup, Doina","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009680404","display_name":"Theodore J. Perkins","orcid":"https://orcid.org/0000-0002-6622-8003"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Perkins, Theodore J.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5126197663"],"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":0.9014000296592712,"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":0.9014000296592712,"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":0.009399999864399433,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.009200000204145908,"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":0.8755000233650208},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5985000133514404},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.3921000063419342},{"id":"https://openalex.org/keywords/error-driven-learning","display_name":"Error-driven learning","score":0.3659000098705292},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.30399999022483826}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8755000233650208},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6938999891281128},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5985000133514404},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4799000024795532},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3815000057220459},{"id":"https://openalex.org/C47932503","wikidata":"https://www.wikidata.org/wiki/Q5395689","display_name":"Error-driven learning","level":3,"score":0.3659000098705292},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3336000144481659},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C41550386","wikidata":"https://www.wikidata.org/wiki/Q529909","display_name":"Multi-agent system","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.14559","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.14559","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":"doi:10.48550/arxiv.2602.14559","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.14559","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":"article"},"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],"primary":[1],"focus":[2],"of":[3,17,30,95,107],"multi-agent":[4],"reinforcement":[5],"learning":[6],"(MARL)":[7],"has":[8],"been":[9],"to":[10,44,72,157],"study":[11],"interactions":[12],"among":[13],"a":[14,37,50,55,60,67,79,124],"fixed":[15,34],"number":[16,29],"agents":[18,31,47,71,117],"embedded":[19],"in":[20,24,141],"an":[21,40],"environment.":[22,81],"However,":[23],"the":[25,28,93],"real":[26],"world,":[27],"is":[32],"neither":[33],"nor":[35],"known":[36],"priori.":[38],"Moreover,":[39],"agent":[41,150],"can":[42,118,133],"decide":[43],"create":[45,73],"other":[46,74],"(for":[48],"example,":[49],"cell":[51],"may":[52,57],"divide,":[53],"or":[54],"company":[56],"spin":[58],"off":[59],"division).":[61],"In":[62],"this":[63,78,100,147],"paper,":[64],"we":[65,76,127],"propose":[66],"framework":[68,148],"that":[69,129,146,152],"allows":[70],"agents;":[75],"call":[77],"fluid-agent":[80,88],"We":[82,144],"present":[83],"game-theoretic":[84],"solution":[85,136],"concepts":[86],"for":[87],"games":[89],"and":[90,113],"empirically":[91],"evaluate":[92],"performance":[94],"several":[96],"MARL":[97],"algorithms":[98],"within":[99],"framework.":[101],"Our":[102],"experiments":[103],"include":[104],"fluid":[105],"variants":[106],"established":[108],"benchmarks":[109],"such":[110],"as":[111,121,123],"Predator-Prey":[112],"Level-Based":[114],"Foraging,":[115],"where":[116],"dynamically":[119,156],"spawn,":[120],"well":[122],"new":[125],"environment":[126],"introduce":[128],"highlights":[130],"how":[131],"fluidity":[132],"unlock":[134],"novel":[135],"strategies":[137],"beyond":[138],"those":[139],"observed":[140],"fixed-population":[142],"settings.":[143],"demonstrate":[145],"yields":[149],"teams":[151],"adjust":[153],"their":[154],"size":[155],"match":[158],"environmental":[159],"demands.":[160]},"counts_by_year":[],"updated_date":"2026-02-18T06:25:47.457606","created_date":"2026-02-18T00:00:00"}