{"id":"https://openalex.org/W4398192319","doi":"https://doi.org/10.48550/arxiv.2405.11778","title":"Efficient Multi-agent Reinforcement Learning by Planning","display_name":"Efficient Multi-agent Reinforcement Learning by Planning","publication_year":2024,"publication_date":"2024-05-20","ids":{"openalex":"https://openalex.org/W4398192319","doi":"https://doi.org/10.48550/arxiv.2405.11778"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2405.11778","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.11778","pdf_url":"https://arxiv.org/pdf/2405.11778","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2405.11778","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056755960","display_name":"Qihan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054624158","display_name":"Jianing Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Jianing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027912723","display_name":"Xiaoteng Ma","orcid":"https://orcid.org/0000-0002-7250-6268"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Xiaoteng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043622784","display_name":"Jun Yang","orcid":"https://orcid.org/0000-0002-7920-2777"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063075000","display_name":"Bin Liang","orcid":"https://orcid.org/0000-0001-7234-1347"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010176958","display_name":"Chongjie Zhang","orcid":"https://orcid.org/0000-0001-8068-0162"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chongjie","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":true,"cited_by_count":1,"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":0.4832000136375427,"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":0.4832000136375427,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7763672471046448},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.639843761920929},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43741488456726074},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29271355271339417},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.20503351092338562},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.08585226535797119}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7763672471046448},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.639843761920929},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43741488456726074},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29271355271339417},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.20503351092338562},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.08585226535797119}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2405.11778","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.11778","pdf_url":"https://arxiv.org/pdf/2405.11778","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"doi:10.48550/arxiv.2405.11778","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.11778","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2405.11778","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.11778","pdf_url":"https://arxiv.org/pdf/2405.11778","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2920061524","https://openalex.org/W4310083477","https://openalex.org/W2328553770","https://openalex.org/W1977959518","https://openalex.org/W2038908348","https://openalex.org/W2107890255","https://openalex.org/W2106552856","https://openalex.org/W2145821588"],"abstract_inverted_index":{"Multi-agent":[0],"reinforcement":[1,35],"learning":[2,36],"(MARL)":[3],"algorithms":[4,18,39],"have":[5],"accomplished":[6],"remarkable":[7],"breakthroughs":[8],"in":[9,28,52,139,172,187],"solving":[10],"large-scale":[11],"decision-making":[12],"tasks.":[13,54],"Nonetheless,":[14],"most":[15],"existing":[16,184],"MARL":[17,64],"are":[19],"model-free,":[20],"limiting":[21],"sample":[22,61,175,191],"efficiency":[23,62,138,176],"and":[24,72,132,155,177,192],"hindering":[25],"their":[26],"applicability":[27],"more":[29],"challenging":[30],"scenarios.":[31],"In":[32],"contrast,":[33],"model-based":[34,67,185],"(MBRL),":[37],"particularly":[38],"integrating":[40],"planning,":[41],"such":[42],"as":[43],"MuZero,":[44],"has":[45],"demonstrated":[46],"superhuman":[47],"performance":[48,182],"with":[49,113,142],"limited":[50],"data":[51],"many":[53],"Hence,":[55],"we":[56,103,146],"aim":[57],"to":[58,96,128],"boost":[59],"the":[60,91,105,163],"of":[63,85,94,174,189],"by":[65],"adopting":[66],"approaches.":[68],"However,":[69],"incorporating":[70],"planning":[71],"search":[73,137],"methods":[74,186],"into":[75],"multi-agent":[76,86],"systems":[77,87],"poses":[78],"significant":[79],"challenges.":[80],"The":[81],"expansive":[82],"action":[83,144],"space":[84],"often":[88],"necessitates":[89],"leveraging":[90],"nearly-independent":[92],"property":[93],"agents":[95],"accelerate":[97],"learning.":[98],"To":[99,135],"tackle":[100],"this":[101],"issue,":[102],"propose":[104],"MAZero":[106,168],"algorithm,":[107],"which":[108],"combines":[109],"a":[110,124],"centralized":[111],"model":[112],"Monte":[114],"Carlo":[115],"Tree":[116],"Search":[117,152],"(MCTS)":[118],"for":[119],"policy":[120],"search.":[121],"We":[122],"design":[123],"novel":[125,149],"network":[126],"structure":[127],"facilitate":[129],"distributed":[130],"execution":[131],"parameter":[133],"sharing.":[134],"enhance":[136],"deterministic":[140],"environments":[141],"sizable":[143],"spaces,":[145],"introduce":[147],"two":[148],"techniques:":[150],"Optimistic":[151],"Lambda":[153],"(OS($\u03bb$))":[154],"Advantage-Weighted":[156],"Policy":[157],"Optimization":[158],"(AWPO).":[159],"Extensive":[160],"experiments":[161],"on":[162],"SMAC":[164],"benchmark":[165],"demonstrate":[166],"that":[167],"outperforms":[169],"model-free":[170],"approaches":[171],"terms":[173,188],"provides":[178],"comparable":[179],"or":[180],"better":[181],"than":[183],"both":[190],"computational":[193],"efficiency.":[194],"Our":[195],"code":[196],"is":[197],"available":[198],"at":[199],"https://github.com/liuqh16/MAZero.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
