{"id":"https://openalex.org/W7165026446","doi":"https://doi.org/10.48550/arxiv.2606.18132","title":"Knowledge Reutilization in Meta-Reinforcement Learning","display_name":"Knowledge Reutilization in Meta-Reinforcement Learning","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165026446","doi":"https://doi.org/10.48550/arxiv.2606.18132"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.18132","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18132","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":"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.2606.18132","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138779464","display_name":"Yuan Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138790111","display_name":"Bo Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013281366","display_name":"J Guerra Ruiz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruiz, Juan de los Rios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073985865","display_name":"Xiangtong Yao","orcid":"https://orcid.org/0000-0003-2556-3072"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Xiangtong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060444894","display_name":"Zhenshan Bing","orcid":"https://orcid.org/0000-0002-0896-2517"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bing, Zhenshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138822835","display_name":"Fuchun Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Fuchun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138790772","display_name":"Alois Knoll","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Knoll, Alois","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.5598999857902527,"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":0.5598999857902527,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0949999988079071,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.03460000082850456,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/task","display_name":"Task (project management)","score":0.7483000159263611},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6345000267028809},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.607200026512146},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.557200014591217},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5076000094413757},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.4343999922275543},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.42149999737739563},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.4147000014781952}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7680000066757202},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7483000159263611},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6345000267028809},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.607200026512146},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.557200014591217},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5432999730110168},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5076000094413757},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49480000138282776},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4625000059604645},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.4343999922275543},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.42149999737739563},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.4147000014781952},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.39239999651908875},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2653000056743622}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.18132","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18132","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":"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.2606.18132","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18132","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":"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":{"Meta-reinforcement":[0],"learning":[1],"enables":[2],"fast":[3],"adaptation":[4],"by":[5,121],"extracting":[6],"shared":[7],"structure":[8],"from":[9],"related":[10],"tasks,":[11],"but":[12],"existing":[13],"end-to-end":[14],"methods":[15],"often":[16],"couple":[17],"task":[18,28,67,81],"inference":[19],"with":[20,83,126,135],"embodiment-specific":[21,105],"control.":[22],"This":[23],"coupling":[24],"can":[25],"obscure":[26],"non-parametric":[27,62],"semantics,":[29],"reduce":[30],"sample":[31],"efficiency,":[32],"and":[33,51,69,91,130],"limit":[34],"cross-agent":[35],"reuse.":[36],"We":[37],"propose":[38],"a":[39,48,60,70,88,92],"meta-knowledge":[40,99],"reutilization":[41],"framework":[42,58,116],"that":[43,114],"learns":[44],"task-level":[45,75],"knowledge":[46,82],"on":[47,109],"dynamics-simplified":[49],"agent":[50],"transfers":[52],"it":[53],"to":[54,64,73],"heterogeneous":[55],"agents.":[56],"The":[57],"uses":[59],"Bayesian":[61],"prior":[63],"organize":[65],"latent":[66],"modes":[68],"high-level":[71],"policy":[72],"generate":[74],"magnitude":[76],"guidance.":[77],"To":[78],"bridge":[79],"reusable":[80],"different":[84],"embodiments,":[85],"we":[86],"introduce":[87],"semantic-magnitude":[89],"interface":[90],"lightweight":[93],"temporal":[94],"adaptor,":[95],"which":[96],"convert":[97],"frozen":[98],"into":[100],"temporally":[101],"aligned":[102],"subgoals":[103],"for":[104],"low-level":[106],"controllers.":[107],"Experiments":[108],"multiple":[110],"locomotion":[111],"agents":[112],"show":[113],"our":[115],"reduces":[117],"final-step":[118],"tracking":[119],"error":[120],"94.75%":[122],"--":[123],"99.79%":[124],"compared":[125],"recent":[127],"state-of-the-art":[128],"baselines":[129],"achieves":[131],"comparable":[132],"deployment":[133],"performance":[134],"about":[136],"23.8%":[137],"of":[138],"their":[139],"interaction":[140],"data.":[141]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-18T00:00:00"}
