{"id":"https://openalex.org/W4320855027","doi":"https://doi.org/10.48550/arxiv.2302.06548","title":"Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning","display_name":"Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning","publication_year":2023,"publication_date":"2023-02-13","ids":{"openalex":"https://openalex.org/W4320855027","doi":"https://doi.org/10.48550/arxiv.2302.06548"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2302.06548","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.06548","pdf_url":"https://arxiv.org/pdf/2302.06548","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2302.06548","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036561905","display_name":"Bram Grooten","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grooten, Bram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021535037","display_name":"Ghada Sokar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sokar, Ghada","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085736804","display_name":"Shibhansh Dohare","orcid":"https://orcid.org/0000-0002-3796-9347"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dohare, Shibhansh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027401676","display_name":"Elena Mocanu","orcid":"https://orcid.org/0000-0002-0856-579X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mocanu, Elena","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070914351","display_name":"Matthew E. Taylor","orcid":"https://orcid.org/0000-0001-8946-0211"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taylor, Matthew E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022601535","display_name":"Mykola Pechenizkiy","orcid":"https://orcid.org/0000-0003-4955-0743"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pechenizkiy, Mykola","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5011045254","display_name":"Decebal Constantin Mocanu","orcid":"https://orcid.org/0000-0002-5636-7683"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mocanu, Decebal Constantin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9778000116348267,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9778000116348267,"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/T11392","display_name":"Energy Harvesting in Wireless Networks","score":0.975600004196167,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9733999967575073,"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":0.8835307359695435},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8037954568862915},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7717989683151245},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.7063332796096802},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6963740587234497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6092286109924316},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6055979132652283},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.56987464427948},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5176287293434143},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4825989007949829},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41666871309280396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36361753940582275},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.0943511426448822}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8835307359695435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8037954568862915},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7717989683151245},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.7063332796096802},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6963740587234497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6092286109924316},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6055979132652283},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.56987464427948},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5176287293434143},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4825989007949829},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41666871309280396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36361753940582275},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0943511426448822},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2302.06548","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.06548","pdf_url":"https://arxiv.org/pdf/2302.06548","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:ris.utwente.nl:publications/0a23df12-b980-4642-acce-066cdb689d7f","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/0a23df12-b980-4642-acce-066cdb689d7f","pdf_url":"https://ris.utwente.nl/ws/files/363562290/2302.06548v1.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Grooten, B, Sokar, G, Dohare, S, Mocanu, E, Taylor, M E, Pechenizkiy, M & Mocanu, D C 2023 'Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning' ArXiv.org. https://doi.org/10.48550/arXiv.2302.06548","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2302.06548","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2302.06548","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2302.06548","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.06548","pdf_url":"https://arxiv.org/pdf/2302.06548","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309949","display_name":"Canadian Institute for Advanced Research","ror":"https://ror.org/01sdtdd95"},{"id":"https://openalex.org/F4320314000","display_name":"Compute Canada","ror":"https://ror.org/03ty8yr27"},{"id":"https://openalex.org/F4320314212","display_name":"Alberta Machine Intelligence Institute","ror":null},{"id":"https://openalex.org/F4320319946","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27"},{"id":"https://openalex.org/F4320321800","display_name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","ror":"https://ror.org/04jsz6e67"},{"id":"https://openalex.org/F4320322675","display_name":"Mitacs","ror":"https://ror.org/00cjrc276"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4320855027.pdf","grobid_xml":"https://content.openalex.org/works/W4320855027.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2952760143","https://openalex.org/W2017776670","https://openalex.org/W4306904969","https://openalex.org/W2347897961","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855"],"abstract_inverted_index":{"Tomorrow's":[0],"robots":[1],"will":[2],"need":[3,90],"to":[4,31,38,80,91,136,160,183],"distinguish":[5],"useful":[6],"information":[7,25,97,189],"from":[8],"noise":[9],"when":[10],"performing":[11],"different":[12],"tasks.":[13],"A":[14],"household":[15],"robot":[16],"for":[17,171],"instance":[18],"may":[19],"continuously":[20],"receive":[21],"a":[22,35,66,107,154,167],"plethora":[23],"of":[24,82,101,119,177],"about":[26,98],"the":[27,56,73,83,99,102,117,178,185,195,201],"home,":[28],"but":[29],"needs":[30],"focus":[32,137],"on":[33,140],"just":[34],"small":[36],"subset":[37],"successfully":[39],"execute":[40],"its":[41,138],"current":[42],"chore.":[43],"Filtering":[44],"distracting":[45],"inputs":[46],"that":[47,144,187],"contain":[48],"irrelevant":[49],"data":[50],"has":[51],"received":[52],"little":[53],"attention":[54],"in":[55,69,123,203],"reinforcement":[57,70,128],"learning":[58,71,129,169],"literature.":[59],"To":[60],"start":[61],"resolving":[62],"this,":[63],"we":[64,105,165],"formulate":[65],"problem":[67],"setting":[68,170],"called":[72],"$\\textit{extremely":[74],"noisy":[75],"environment}$":[76],"(ENE),":[77],"where":[78],"up":[79,159],"$99\\%$":[81],"input":[84,133],"features":[85,94,176],"are":[86],"pure":[87],"noise.":[88],"Agents":[89],"detect":[92],"which":[93,115],"provide":[95],"task-relevant":[96,141],"state":[100],"environment.":[103],"Consequently,":[104],"propose":[106],"new":[108],"method":[109],"termed":[110],"$\\textit{Automatic":[111],"Noise":[112],"Filtering}$":[113],"(ANF),":[114],"uses":[116],"principles":[118],"dynamic":[120],"sparse":[121,132],"training":[122],"synergy":[124],"with":[125],"various":[126],"deep":[127],"algorithms.":[130],"The":[131],"layer":[134],"learns":[135],"connectivity":[139],"features,":[142],"such":[143],"ANF-SAC":[145],"and":[146,151,206],"ANF-TD3":[147],"outperform":[148],"standard":[149],"SAC":[150],"TD3":[152],"by":[153,173],"large":[155],"margin,":[156],"while":[157],"using":[158],"$95\\%$":[161],"fewer":[162],"weights.":[163],"Furthermore,":[164],"devise":[166],"transfer":[168],"ENEs,":[172],"permuting":[174],"all":[175],"environment":[179],"after":[180],"1M":[181],"timesteps":[182],"simulate":[184],"fact":[186],"other":[188],"sources":[190],"can":[191],"become":[192],"relevant":[193],"as":[194],"world":[196],"evolves.":[197],"Again,":[198],"ANF":[199],"surpasses":[200],"baselines":[202],"final":[204],"performance":[205],"sample":[207],"complexity.":[208],"Our":[209],"code":[210],"is":[211],"available":[212],"at":[213],"https://github.com/bramgrooten/automatic-noise-filtering":[214]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}