{"id":"https://openalex.org/W4416255975","doi":"https://doi.org/10.1109/ijcnn64981.2025.11534054","title":"Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning","display_name":"Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416255975","doi":"https://doi.org/10.1109/ijcnn64981.2025.11534054"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11534054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11534054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.19305","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102776147","display_name":"Yifu Luo","orcid":"https://orcid.org/0009-0005-9850-768X"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifu Luo","raw_affiliation_strings":["Tsinghua University,ShenZhen International Graduate School,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,ShenZhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I4210114105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026500154","display_name":"Yongzhe Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongzhe Chang","raw_affiliation_strings":["Tsinghua University,ShenZhen International Graduate School,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,ShenZhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I4210114105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100737125","display_name":"Xueqian Wang","orcid":"https://orcid.org/0000-0003-3542-0593"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueqian Wang","raw_affiliation_strings":["Tsinghua University,ShenZhen International Graduate School,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,ShenZhen International Graduate School,Shenzhen,China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I4210114105"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.6625999808311462,"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":0.6625999808311462,"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/T10879","display_name":"Robotic Locomotion and Control","score":0.03959999978542328,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10653","display_name":"Robot Manipulation and Learning","score":0.03550000116229057,"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":0.7222999930381775},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6248000264167786},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.5659999847412109},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5640000104904175},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5259000062942505},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45500001311302185},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.428600013256073},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.4140999913215637}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7222999930381775},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6248000264167786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.613099992275238},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.5659999847412109},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5640000104904175},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5259000062942505},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45500001311302185},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44110000133514404},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4146000146865845},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4140999913215637},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.3720000088214874},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3691999912261963},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.36880001425743103},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.3467000126838684},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.30649998784065247},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.2989000082015991},{"id":"https://openalex.org/C95722684","wikidata":"https://www.wikidata.org/wiki/Q2622756","display_name":"Continuous wavelet transform","level":5,"score":0.2913999855518341},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.289000004529953},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2833999991416931},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.27230000495910645}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11534054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11534054","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2509.19305","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2509.19305","pdf_url":"https://arxiv.org/pdf/2509.19305","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.19305","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.19305","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":"pmh:oai:arXiv.org:2509.19305","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2509.19305","pdf_url":"https://arxiv.org/pdf/2509.19305","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4151766453","display_name":null,"funder_award_id":"2024A1515010003","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G8568010469","display_name":null,"funder_award_id":"2022YFB4701400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416255975.pdf","grobid_xml":"https://content.openalex.org/works/W4416255975.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"probability":[1],"models":[2],"have":[3],"shown":[4],"significant":[5],"promise":[6],"in":[7,64,74],"offline":[8],"reinforcement":[9],"learning":[10],"by":[11],"directly":[12],"modeling":[13,112],"trajectory":[14,75],"sequences.":[15],"However,":[16],"existing":[17,158],"approaches":[18,60],"primarily":[19],"focus":[20],"on":[21,136],"time-domain":[22],"features":[23,128],"while":[24],"overlooking":[25],"frequency-domain":[26,127],"features,":[27],"leading":[28,147],"to":[29,36,100,125,148],"frequency":[30,53,70,145],"shift":[31],"and":[32,77,105,121,129,153],"degraded":[33,78],"performance":[34,156],"according":[35],"our":[37],"observation.":[38],"In":[39],"this":[40,82],"paper,":[41],"we":[42,84],"investigate":[43],"the":[44,52,65,69,137],"RL":[45,93],"problem":[46],"from":[47],"a":[48,90],"new":[49],"perspective":[50],"of":[51,68],"domain.":[54],"We":[55],"first":[56],"observe":[57],"that":[58,95,141],"time-domain-only":[59],"inadvertently":[61],"introduce":[62],"shifts":[63],"low-frequency":[66],"components":[67],"domain,":[71],"which":[72],"results":[73,135],"instability":[76],"performance.":[79],"To":[80,108],"address":[81],"issue,":[83],"propose":[85],"Wavelet":[86,98],"Fourier":[87,119],"Diffuser":[88],"(WFDiffuser),":[89],"novel":[91],"diffusion-based":[92],"framework":[94],"integrates":[96],"Discrete":[97],"Transform":[99,120],"decompose":[101],"trajectories":[102,152],"into":[103],"low-":[104],"high-frequency":[106],"components.":[107],"further":[109],"enhance":[110],"diffusion":[111],"for":[113],"each":[114],"component,":[115],"WFDiffuser":[116,142],"employs":[117],"Short-Time":[118],"cross":[122],"attention":[123],"mechanisms":[124],"extract":[126],"facilitate":[130],"cross-frequency":[131],"interaction.":[132],"Extensive":[133],"experiment":[134],"D4RL":[138],"benchmark":[139],"demonstrate":[140],"effectively":[143],"mitigates":[144],"shift,":[146],"smoother,":[149],"more":[150],"stable":[151],"improved":[154],"decision-making":[155],"over":[157],"methods.":[159]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}