{"id":"https://openalex.org/W4385488639","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191108","title":"FastAct: A Lightweight Actor Compression Framework for Fast Policy Learning","display_name":"FastAct: A Lightweight Actor Compression Framework for Fast Policy Learning","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385488639","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191108"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191108","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10191108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100449658","display_name":"Hongjie Zhang","orcid":"https://orcid.org/0000-0001-9565-3378"},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongjie Zhang","raw_affiliation_strings":["College of Computer Science, Sichuan Normal University,Chengdu,China","Sichuan Yuanzhigu Technology Co., Ltd., Chengdu, China","College of Computer Science, Sichuan Normal University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan Normal University,Chengdu,China","institution_ids":["https://openalex.org/I63354593"]},{"raw_affiliation_string":"Sichuan Yuanzhigu Technology Co., Ltd., Chengdu, China","institution_ids":[]},{"raw_affiliation_string":"College of Computer Science, Sichuan Normal University, Chengdu, China","institution_ids":["https://openalex.org/I63354593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115593854","display_name":"Haoming Ma","orcid":"https://orcid.org/0009-0007-5071-0771"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoming Ma","raw_affiliation_strings":["Sun Yat-Sen University,Guangzhou,China","Sun Yat-Sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,Guangzhou,China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100422933","display_name":"Zhenyu Chen","orcid":"https://orcid.org/0000-0002-4989-7109"},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Chen","raw_affiliation_strings":["College of Computer Science, Sichuan Normal University,Chengdu,China","College of Computer Science, Sichuan Normal University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan Normal University,Chengdu,China","institution_ids":["https://openalex.org/I63354593"]},{"raw_affiliation_string":"College of Computer Science, Sichuan Normal University, Chengdu, China","institution_ids":["https://openalex.org/I63354593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100449658"],"corresponding_institution_ids":["https://openalex.org/I63354593"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08512546,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","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":0.9995999932289124,"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":0.9995999932289124,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.992900013923645,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.992900013923645,"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":0.8581584692001343},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8230208158493042},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5477785468101501},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4767656922340393},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45711424946784973},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.43300673365592957},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43036943674087524}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8581584692001343},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8230208158493042},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5477785468101501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4767656922340393},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45711424946784973},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.43300673365592957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43036943674087524},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191108","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10191108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2145339207","https://openalex.org/W2555255624","https://openalex.org/W2601251286","https://openalex.org/W2616729100","https://openalex.org/W2730357202","https://openalex.org/W2766447205","https://openalex.org/W2796155151","https://openalex.org/W2978696242","https://openalex.org/W3087827640","https://openalex.org/W3107951310","https://openalex.org/W3173470024","https://openalex.org/W4213377513","https://openalex.org/W4221163429","https://openalex.org/W4288095758","https://openalex.org/W4290944609","https://openalex.org/W4302010773","https://openalex.org/W4308523432","https://openalex.org/W6740640171","https://openalex.org/W6747481501","https://openalex.org/W6748554570","https://openalex.org/W6748638692","https://openalex.org/W6752963931","https://openalex.org/W6769580333","https://openalex.org/W6780153092","https://openalex.org/W6783347321","https://openalex.org/W6785535465","https://openalex.org/W6789390734","https://openalex.org/W6810083960"],"related_works":["https://openalex.org/W4287880334","https://openalex.org/W4306904969","https://openalex.org/W4366700029","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W2031695474","https://openalex.org/W4285230481","https://openalex.org/W2024136090","https://openalex.org/W4391331176"],"abstract_inverted_index":{"Deep":[0],"reinforcement":[1],"learning":[2,30,47],"outperforms":[3],"humans":[4],"in":[5],"a":[6,44,191],"variety":[7],"of":[8,19,109,116,135,152],"complex":[9],"decision-making":[10],"tasks.":[11],"However,":[12],"training":[13,53,183],"an":[14],"effective":[15],"policy":[16,46,52,76],"requires":[17],"millions":[18],"interactions":[20],"between":[21],"the":[22,25,29,62,80,83,98,106,110,114,117,122,127,132,136,142,149,153,174],"agent":[23],"and":[24,34,89,102,162],"target":[26,154],"environment,":[27],"making":[28],"process":[31],"time":[32,184],"consuming":[33],"resource":[35],"intensive.":[36],"Inspired":[37],"by":[38,54,64,176,185],"neural":[39],"network":[40],"compression,":[41],"we":[42,120],"propose":[43],"lightweight":[45],"framework,":[48,82],"FastAct,":[49],"to":[50,70,79,104,124,131,147,178,187],"accelerate":[51],"speeding":[55],"up":[56,173],"Actor":[57,63,175],"inference.":[58],"Specifically,":[59],"FastAct":[60,84,140,158,171,181],"accelerates":[61],"dynamically":[65,125],"using":[66],"multiple":[67,94],"compression":[68,95,128],"techniques":[69],"quickly":[71],"generate":[72],"experience":[73,118],"data":[74],"for":[75],"training.":[77],"Compared":[78,168],"traditional":[81],"adds":[85],"two":[86],"modules,":[87],"Compressor":[88,92],"Scheduler.":[90],"The":[91],"integrates":[93],"algorithms":[96],"through":[97],"chain-of-responsibility":[99],"design":[100],"pattern":[101],"aims":[103],"maximize":[105],"inference":[107],"speed":[108],"Actors.":[111],"To":[112],"ensure":[113],"quality":[115],"data,":[119],"develop":[121],"Scheduler":[123],"adjust":[126],"algorithm":[129],"according":[130],"statistical":[133],"information":[134],"Actor.":[137],"In":[138],"addition,":[139],"applies":[141],"off-policy":[143],"value":[144,150],"estimator":[145],"V-trace":[146],"correct":[148],"function":[151],"policy.":[155],"We":[156],"implemented":[157],"based":[159],"on":[160,166],"IMPALA":[161],"conducted":[163],"extensive":[164],"experiments":[165],"Atari.":[167],"with":[169],"IMPALA,":[170],"speeds":[172],"2.0x":[177],"4.1x.":[179],"And":[180],"reduces":[182],"29.8%":[186],"40.3%":[188],"while":[189],"maintaining":[190],"similar":[192],"result.":[193]},"counts_by_year":[],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}
