{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T02:47:27Z","timestamp":1781837247960,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T00:00:00Z","timestamp":1667088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CF-2028879; CCF-1640081"],"award-info":[{"award-number":["CF-2028879; CCF-1640081"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000028","name":"Semiconductor Research Corporation","doi-asserted-by":"publisher","award":["ASCENT"],"award-info":[{"award-number":["ASCENT"]}],"id":[{"id":"10.13039\/100000028","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,30]]},"DOI":"10.1145\/3508352.3549387","type":"proceedings-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T12:10:54Z","timestamp":1671711054000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Associative Memory Based Experience Replay for Deep Reinforcement Learning"],"prefix":"10.1145","author":[{"given":"Mengyuan","family":"Li","sequence":"first","affiliation":[{"name":"University of Notre Dame"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arman","family":"Kazemi","sequence":"additional","affiliation":[{"name":"University of Notre Dame"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ann Franchesca","family":"Laguna","sequence":"additional","affiliation":[{"name":"De La Salle University, Manila, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"X. Sharon","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Notre Dame"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Machine Learning. PMLR, 507--517","author":"Badia Adri\u00e0 Puigdom\u00e8nech","year":"2020","unstructured":"Adri\u00e0 Puigdom\u00e8nech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Zhaohan Daniel Guo, and Charles Blundell. 2020. Agent57: Outperforming the atari human benchmark. In International Conference on Machine Learning. PMLR, 507--517."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3085572"},{"key":"e_1_3_2_1_3_1","unstructured":"Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. arXiv:arXiv:1606.01540"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEDM19574.2021.9720495"},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Machine Learning. PMLR, 3061--3071","author":"Fedus William","year":"2020","unstructured":"William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, and Will Dabney. 2020. Revisiting fundamentals of experience replay. In International Conference on Machine Learning. PMLR, 3061--3071."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Tuomas Haarnoja Sehoon Ha Aurick Zhou Jie Tan George Tucker and Sergey Levine. 2019. Learning to Walk Via Deep Reinforcement Learning.. In Robotics: Science and Systems.","DOI":"10.15607\/RSS.2019.XV.011"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11796"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEDM19574.2021.9720562"},{"key":"e_1_3_2_1_10_1","volume-title":"In-memory computing with resistive switching devices. Nature electronics 1, 6","author":"Ielmini Daniele","year":"2018","unstructured":"Daniele Ielmini and H-S Philip Wong. 2018. In-memory computing with resistive switching devices. Nature electronics 1, 6 (2018), 333--343."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2019.2952544"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-020-0410-3"},{"key":"e_1_3_2_1_13_1","volume-title":"Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1084--1089","author":"Kazemi Arman","year":"2021","unstructured":"Arman Kazemi, Mohammad Mehdi Sharifi, Ann Franchesca Laguna, Franz M\u00fcller, Ramin Rajaei, Ricardo Olivo, Thomas K\u00e4mpfe, Michael Niemier, and X Sharon Hu. 2021. In-memory nearest neighbor search with fefet multi-bit content-addressable memories. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1084--1089."},{"key":"e_1_3_2_1_14_1","volume-title":"Ann Franchesca Balon Laguna, Franz Muller, Xunzhao Yin, Thomas Kampfe, Michael Niemier, and X Sharon Hu.","author":"Kazemi Arman","year":"2021","unstructured":"Arman Kazemi, Mohammad Mehdi Sharifi, Ann Franchesca Balon Laguna, Franz Muller, Xunzhao Yin, Thomas Kampfe, Michael Niemier, and X Sharon Hu. 2021. FeFET Multi-Bit Content-Addressable Memories for In-Memory Nearest Neighbor Search. IEEE Trans. Comput. (2021)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3307650.3322259"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei A Rusu Joel Veness Marc G Bellemare Alex Graves Martin Riedmiller Andreas K Fidjeland Georg Ostrovski et al. 2015. Human-level control through deep reinforcement learning. Nature 518 7540 (2015) 529--533.","DOI":"10.1038\/nature14236"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41928-019-0321-3"},{"key":"e_1_3_2_1_18_1","volume-title":"Tree-based machine learning performed in-memory with memristive analog CAM. Nature communications 12, 1","author":"Pedretti Giacomo","year":"2021","unstructured":"Giacomo Pedretti, Catherine E Graves, Sergey Serebryakov, Ruibin Mao, Xia Sheng, Martin Foltin, Can Li, and John Paul Strachan. 2021. Tree-based machine learning performed in-memory with memristive analog CAM. Nature communications 12, 1 (2021), 1--10."},{"key":"e_1_3_2_1_19_1","volume-title":"Prioritized experience replay. arXiv preprint:1511.05952","author":"Schaul Tom","year":"2015","unstructured":"Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. 2015. Prioritized experience replay. arXiv preprint:1511.05952 (2015)."},{"key":"e_1_3_2_1_20_1","volume-title":"Riduan Khaddam-Aljameh, and Evangelos Eleftheriou.","author":"Sebastian Abu","year":"2020","unstructured":"Abu Sebastian, Manuel Le Gallo, Riduan Khaddam-Aljameh, and Evangelos Eleftheriou. 2020. Memory devices and applications for in-memory computing. Nature nanotechnology 15, 7 (2020), 529--544."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3039902.3039915"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Ying Wang Mengdi Wang Bing Li Huawei Li and Xiaowei Li. 2020. A many-core accelerator design for on-chip deep reinforcement learning. In ICCAD. 1--7.","DOI":"10.1145\/3400302.3415636"}],"event":{"name":"ICCAD '22: IEEE\/ACM International Conference on Computer-Aided Design","location":"San Diego California","acronym":"ICCAD '22","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE-EDS Electronic Devices Society","IEEE CAS","IEEE CEDA"]},"container-title":["Proceedings of the 41st IEEE\/ACM International Conference on Computer-Aided Design"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508352.3549387","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3508352.3549387","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3508352.3549387","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:57Z","timestamp":1750186977000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508352.3549387"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,30]]},"references-count":22,"alternative-id":["10.1145\/3508352.3549387","10.1145\/3508352"],"URL":"https:\/\/doi.org\/10.1145\/3508352.3549387","relation":{},"subject":[],"published":{"date-parts":[[2022,10,30]]},"assertion":[{"value":"2022-12-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}