{"id":"https://openalex.org/W7163400860","doi":"https://doi.org/10.48550/arxiv.2606.03335","title":"GPU-Parallel Multi-Task Reinforcement Learning with Demonstration Guided Policy Optimization","display_name":"GPU-Parallel Multi-Task Reinforcement Learning with Demonstration Guided Policy Optimization","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163400860","doi":"https://doi.org/10.48550/arxiv.2606.03335"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.03335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03335","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.03335","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137805400","display_name":"Rui Zhang (13940)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137784851","display_name":"Qiwei Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Qiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100601501","display_name":"Zhengyu Zhang","orcid":"https://orcid.org/0000-0003-2940-5995"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhengyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137786664","display_name":"Tao Li (86810)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Tao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137807168","display_name":"Yunrong Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yunrong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137730968","display_name":"Junjie Lai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lai, Junjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137714763","display_name":"Renjing Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Renjing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137736955","display_name":"Weihua Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Weihua","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":0.7490000128746033,"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":0.7490000128746033,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.1891999989748001,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12290","display_name":"Human Motion and Animation","score":0.005400000140070915,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.9010000228881836},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7477999925613403},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6757000088691711},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5331000089645386},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.507099986076355},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.4284000098705292},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3955000042915344}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.9010000228881836},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7477999925613403},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7307000160217285},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6757000088691711},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6312000155448914},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5519999861717224},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5331000089645386},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.507099986076355},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4284000098705292},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3955000042915344},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.3546999990940094},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.28600001335144043},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.2630999982357025},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C121050878","wikidata":"https://www.wikidata.org/wiki/Q5135020","display_name":"Cloning (programming)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.03335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03335","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.03335","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.03335","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":"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":{"Large":[0],"scale":[1],"GPU-parallel":[2,36],"reinforcement":[3,59],"learning":[4,60],"has":[5],"changed":[6],"what":[7],"can":[8],"be":[9],"trained":[10],"in":[11,51],"robot":[12],"simulation,":[13],"yet":[14],"most":[15],"systems":[16],"still":[17],"optimize":[18],"one":[19],"specialist":[20],"policy":[21],"per":[22],"task.":[23],"We":[24],"propose":[25,90],"a":[26,112],"construction":[27],"methodology":[28],"for":[29],"turning":[30],"structured":[31],"manipulation":[32],"task":[33,49,63,117],"families":[34],"into":[35],"multi-task":[37],"RL":[38,122],"benchmarks,":[39],"and":[40,48,70,84,123,131],"instantiate":[41],"it":[42],"as":[43],"MT-Libero":[44],"using":[45],"LIBERO":[46],"assets":[47],"predicates":[50],"Isaac":[52],"Lab.":[53],"The":[54],"resulting":[55],"benchmark":[56],"supports":[57],"simultaneous":[58],"over":[61],"heterogeneous":[62],"suites":[64],"with":[65,102],"parallel":[66],"rendering,":[67],"physics":[68],"randomization,":[69],"state-input":[71],"or":[72],"visual-input":[73],"policies.":[74],"To":[75],"make":[76],"such":[77],"training":[78],"practical":[79],"under":[80],"sparse":[81],"success":[82],"signals":[83],"limited":[85],"prior":[86],"data,":[87],"we":[88],"further":[89],"DGPO,":[91],"an":[92],"on-policy":[93,136],"demonstration":[94,108],"guided":[95],"method":[96],"that":[97],"combines":[98],"importance":[99],"weighted":[100],"PPO":[101],"adaptive":[103],"behavior":[104],"cloning":[105],"on":[106],"matched":[107],"actions.":[109],"DGPO":[110],"enables":[111],"tunable":[113],"preference":[114],"toward":[115],"demonstrated":[116],"distributions,":[118],"outperforming":[119],"both":[120],"prior-free":[121],"existing":[124],"demonstration-based":[125],"methods":[126],"while":[127],"preserving":[128],"the":[129],"stability":[130],"online":[132],"improvement":[133],"benefits":[134],"of":[135],"PPO.":[137]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-04T00:00:00"}