{"id":"https://openalex.org/W7161067713","doi":"https://doi.org/10.48550/arxiv.2605.12261","title":"Delay-Empowered Causal Hierarchical Reinforcement Learning","display_name":"Delay-Empowered Causal Hierarchical Reinforcement Learning","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161067713","doi":"https://doi.org/10.48550/arxiv.2605.12261"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.12261","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12261","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":null,"license_id":null,"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.2605.12261","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016270698","display_name":"Chenran Zhao","orcid":"https://orcid.org/0009-0008-6261-9585"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Chenran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019758816","display_name":"Dianxi Shi","orcid":"https://orcid.org/0000-0002-8112-371X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Dianxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136066991","display_name":"Haotian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haotian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057464374","display_name":"Mengzhu Wang","orcid":"https://orcid.org/0000-0002-1059-0441"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Mengzhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100677019","display_name":"Yaowen Zhang","orcid":"https://orcid.org/0000-0002-1379-2639"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yaowen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072647192","display_name":"Chunping Qiu","orcid":"https://orcid.org/0000-0002-7109-5559"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Chunping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136075431","display_name":"Shaowu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Shaowu","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":0.5932000279426575,"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":0.5932000279426575,"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/T13553","display_name":"Age of Information Optimization","score":0.05640000104904175,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.038100000470876694,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.8345999717712402},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4749999940395355},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4399000108242035},{"id":"https://openalex.org/keywords/temporal-difference-learning","display_name":"Temporal difference learning","score":0.3862000107765198},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.38089999556541443},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.35830000042915344}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8345999717712402},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7141000032424927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5913000106811523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4763000011444092},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4749999940395355},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4399000108242035},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.3862000107765198},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.35830000042915344},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.2651999890804291}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.12261","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12261","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.12261","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12261","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.8172190189361572,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Many":[0],"real-world":[1],"tasks":[2],"involve":[3],"delayed":[4],"effects,":[5],"where":[6],"the":[7,79],"outcomes":[8],"of":[9,28,82],"actions":[10],"emerge":[11],"after":[12],"varying":[13],"time":[14],"lags.":[15],"Existing":[16],"delay-aware":[17,97],"reinforcement":[18,40],"learning":[19],"methods":[20,57],"often":[21],"rely":[22],"on":[23],"state":[24,83],"augmentation,":[25],"prior":[26],"knowledge":[27],"delay":[29,89],"distributions,":[30],"or":[31],"access":[32],"to":[33,51,60],"non-delayed":[34],"data,":[35],"limiting":[36],"their":[37,86],"generalization.":[38],"Hierarchical":[39,71],"learning,":[41],"by":[42],"contrast,":[43],"inherently":[44],"offers":[45],"advantages":[46],"in":[47,117,139],"handling":[48],"delays":[49,134],"due":[50],"its":[52],"hierarchical":[53],"structure,":[54],"yet":[55],"existing":[56],"are":[58,92],"restricted":[59],"fixed":[61],"delays.":[62,125],"To":[63],"address":[64],"these":[65],"limitations,":[66],"we":[67],"propose":[68],"Delay-Empowered":[69],"Causal":[70],"Reinforcement":[72],"Learning":[73],"(DECHRL).":[74],"DECHRL":[75,116,130],"explicitly":[76],"models":[77,132],"both":[78],"causal":[80],"structure":[81],"transitions":[84],"and":[85,120,135],"associated":[87],"stochastic":[88,124],"distributions.":[90],"These":[91],"then":[93],"incorporated":[94],"into":[95],"a":[96],"empowerment":[98],"objective":[99],"that":[100,129],"drives":[101],"proactive":[102],"exploration":[103],"toward":[104],"highly":[105],"controllable":[106],"states,":[107],"thereby":[108],"improving":[109],"performance":[110],"under":[111,141],"temporal":[112,133,142],"uncertainty.":[113,143],"We":[114],"evaluate":[115],"modified":[118],"2D-Minecraft":[119],"MiniGrid":[121],"environments":[122],"featuring":[123],"Experimental":[126],"results":[127],"show":[128],"effectively":[131],"significantly":[136],"outperforms":[137],"baselines":[138],"decision-making":[140]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}