{"id":"https://openalex.org/W4312121068","doi":"https://doi.org/10.1145/3508352.3549387","title":"Associative Memory Based Experience Replay for Deep Reinforcement Learning","display_name":"Associative Memory Based Experience Replay for Deep Reinforcement Learning","publication_year":2022,"publication_date":"2022-10-30","ids":{"openalex":"https://openalex.org/W4312121068","doi":"https://doi.org/10.1145/3508352.3549387"},"language":"en","primary_location":{"id":"doi:10.1145/3508352.3549387","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3508352.3549387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100415393","display_name":"Mengyuan Li","orcid":"https://orcid.org/0000-0001-5526-3228"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mengyuan Li","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003063931","display_name":"Arman Kazemi","orcid":"https://orcid.org/0000-0002-2009-5516"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arman Kazemi","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053712676","display_name":"Ann Franchesca Laguna","orcid":"https://orcid.org/0000-0001-8267-1040"},"institutions":[{"id":"https://openalex.org/I5996616","display_name":"De La Salle University","ror":"https://ror.org/04xftk194","country_code":"PH","type":"education","lineage":["https://openalex.org/I5996616"]}],"countries":["PH"],"is_corresponding":false,"raw_author_name":"Ann Franchesca Laguna","raw_affiliation_strings":["De La Salle University, Manila, Philippines"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"De La Salle University, Manila, Philippines","institution_ids":["https://openalex.org/I5996616"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100600905","display_name":"Xiaobo Sharon Hu","orcid":"https://orcid.org/0000-0002-6636-9738"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"X. Sharon Hu","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3485,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.83674614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9940000176429749,"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":0.9940000176429749,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9765999913215637,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13219","display_name":"Mind wandering and attention","score":0.9743000268936157,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8690181970596313},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7391878962516785},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.6315352916717529},{"id":"https://openalex.org/keywords/tree-traversal","display_name":"Tree traversal","score":0.5650882720947266},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.4918191730976105},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.47611555457115173},{"id":"https://openalex.org/keywords/content-addressable-memory","display_name":"Content-addressable memory","score":0.4488902986049652},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3649337887763977},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.3418438136577606},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.27996015548706055},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2765249013900757},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17810410261154175}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8690181970596313},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7391878962516785},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.6315352916717529},{"id":"https://openalex.org/C140745168","wikidata":"https://www.wikidata.org/wiki/Q1210082","display_name":"Tree traversal","level":2,"score":0.5650882720947266},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4918191730976105},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.47611555457115173},{"id":"https://openalex.org/C53442348","wikidata":"https://www.wikidata.org/wiki/Q745101","display_name":"Content-addressable memory","level":3,"score":0.4488902986049652},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3649337887763977},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.3418438136577606},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.27996015548706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2765249013900757},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17810410261154175},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3508352.3549387","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3508352.3549387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2248569881","display_name":"Collaborative Research: PPoSS: Planning: S3-IoT: Design and Deployment of Scalable, Secure, and Smart Mission-Critical IoT Systems","funder_award_id":"2028879","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306087","display_name":"Semiconductor Research Corporation","ror":"https://ror.org/047z4n946"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2145339207","https://openalex.org/W2285660444","https://openalex.org/W2572023225","https://openalex.org/W2725159389","https://openalex.org/W2761873684","https://openalex.org/W2907537824","https://openalex.org/W2949161920","https://openalex.org/W2983032988","https://openalex.org/W2988640543","https://openalex.org/W3032819016","https://openalex.org/W3111884232","https://openalex.org/W4225916833","https://openalex.org/W4226092120"],"related_works":["https://openalex.org/W170547082","https://openalex.org/W2136735429","https://openalex.org/W2587876411","https://openalex.org/W2358332176","https://openalex.org/W4301044568","https://openalex.org/W2128075414","https://openalex.org/W2952867832","https://openalex.org/W151008969","https://openalex.org/W2365664007","https://openalex.org/W3107197685"],"abstract_inverted_index":{"Experience":[0],"replay":[1,29],"is":[2],"an":[3,73,81,104],"essential":[4],"component":[5],"in":[6,23,40,94],"deep":[7],"reinforcement":[8],"learning":[9,99,124],"(DRL),":[10],"which":[11],"stores":[12],"the":[13,19,88,98,136,141],"experiences":[14,17],"and":[15,37,60],"generates":[16],"for":[18],"agent":[20],"to":[21,34,57,71,112,129,140],"learn":[22],"real":[24],"time.":[25],"Recently,":[26],"prioritized":[27],"experience":[28],"(PER)":[30],"has":[31],"been":[32],"proven":[33],"be":[35],"powerful":[36],"widely":[38],"deployed":[39],"DRL":[41],"agents.":[42],"However,":[43],"implementing":[44],"PER":[45,95,143],"on":[46,110,135,145],"traditional":[47],"CPU":[48],"or":[49],"GPU":[50],"architectures":[51],"incurs":[52],"significant":[53],"latency":[54,131],"overhead":[55],"due":[56],"its":[58],"frequent":[59],"irregular":[61],"memory":[62,75],"accesses.":[63],"This":[64],"paper":[65],"proposes":[66],"a":[67],"hardware-software":[68],"co-design":[69],"approach":[70],"design":[72,103],"associative":[74],"(AM)":[76],"based":[77,109],"PER,":[78],"AMPER,":[79],"with":[80],"AM-friendly":[82],"priority":[83,92],"sampling":[84,93],"operation.":[85],"AMPER":[86,114,121],"replaces":[87],"widely-used":[89],"time-costly":[90],"tree-traversal-based":[91],"while":[96,126],"preserving":[97],"performance.":[100],"Further,":[101],"we":[102],"in-memory":[105,118],"computing":[106],"hardware":[107,138],"architecture":[108],"AM":[111],"support":[113],"by":[115],"leveraging":[116],"parallel":[117],"search":[119],"operations.":[120],"shows":[122],"comparable":[123],"performance":[125],"achieving":[127],"55\u00d7":[128],"270\u00d7":[130],"improvement":[132],"when":[133],"running":[134,144],"proposed":[137],"compared":[139],"state-of-the-art":[142],"GPU.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}