{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T03:17:01Z","timestamp":1774322221410,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["101019982"],"award-info":[{"award-number":["101019982"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,23]]},"DOI":"10.1145\/3649329.3657335","type":"proceedings-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T19:27:22Z","timestamp":1731007642000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Using Probabilistic Model Rollouts to Boost the Sample Efficiency of Reinforcement Learning for Automated Analog Circuit Sizing"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1113-092X","authenticated-orcid":false,"given":"Mohsen","family":"Ahmadzadeh","sequence":"first","affiliation":[{"name":"ESAT-MICAS, KU Leuven, Leuven, Flemish Brabant, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4061-9428","authenticated-orcid":false,"given":"Georges G. E.","family":"Gielen","sequence":"additional","affiliation":[{"name":"ESAT-MICAS, KU Leuven, Leuven, Flemish Brabant, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"e_1_3_2_1_1_1","series-title":"Studies in Computational Intelligence","volume-title":"Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques","author":"Manuel Barros","unstructured":"Manuel Barros et al. Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques, volume 294 of Studies in Computational Intelligence. Springer Berlin Heidelberg."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.899053"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2002.806600"},{"key":"e_1_3_2_1_4_1","first-page":"660","volume-title":"Automation & Test in Europe Conference & Exhibition (DATE)","author":"Tiago","unstructured":"Tiago Pessoa et al. Enhanced analog and RF IC sizing methodology using PCA and NSGA-II optimization kernel. In 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 660--665. IEEE."},{"key":"e_1_3_2_1_5_1","first-page":"3306","volume-title":"International conference on machine learning","author":"Wenlong","year":"2018","unstructured":"Wenlong Lyu et al. Batch bayesian optimization via multi-objective acquisition ensemble for automated analog circuit design. In International conference on machine learning, pages 3306--3314. PMLR, 2018."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2013.2284109"},{"key":"e_1_3_2_1_7_1","first-page":"490","volume-title":"Automation & Test in Europe Conference & Exhibition (DATE)","author":"Keertana","year":"2020","unstructured":"Keertana Settaluri et al. Autockt: Deep reinforcement learning of analog circuit designs. In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 490--495. IEEE, 2020."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"e_1_3_2_1_9_1","volume-title":"A tutorial on bayesian optimization. arXiv preprint arXiv:1807.02811","author":"Peter Frazier","year":"2018","unstructured":"Peter Frazier et al. A tutorial on bayesian optimization. arXiv preprint arXiv:1807.02811, 2018."},{"key":"e_1_3_2_1_10_1","volume-title":"NeurIPS Machine Learning for Systems Workshop","author":"Hanrui","year":"2018","unstructured":"Hanrui Wang et al. Learning to design circuits. In NeurIPS Machine Learning for Systems Workshop, 2018."},{"key":"e_1_3_2_1_11_1","first-page":"1","volume-title":"2020 57th ACM\/IEEE Design Automation Conference (DAC)","author":"Hanrui","year":"2020","unstructured":"Hanrui Wang et al. GCN-RL circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learning. In 2020 57th ACM\/IEEE Design Automation Conference (DAC), pages 1--6. IEEE, 2020."},{"key":"e_1_3_2_1_12_1","first-page":"1231","volume-title":"2021 58th ACM\/IEEE Design Automation Conference (DAC)","author":"Karthik Somayaji N.S.","unstructured":"N.S. Karthik Somayaji, Hanbin Hu, and Peng Li. Prioritized reinforcement learning for analog circuit optimization with design knowledge. In 2021 58th ACM\/IEEE Design Automation Conference (DAC), pages 1231--1236. IEEE."},{"key":"e_1_3_2_1_13_1","volume-title":"2021 58th ACM\/IEEE Design Automation Conference (DAC). IEEE.","author":"Kai","unstructured":"Kai Yang et al. Trust-region method with deep reinforcement learning in analog design space exploration. In 2021 58th ACM\/IEEE Design Automation Conference (DAC). IEEE."},{"key":"e_1_3_2_1_14_1","first-page":"35","volume-title":"Proceedings of the 2022 ACM\/IEEE Workshop on Machine Learning for CAD","author":"Wei","year":"2022","unstructured":"Wei Shi et al. Robustanalog: Fast variation-aware analog circuit design via multi-task rl. In Proceedings of the 2022 ACM\/IEEE Workshop on Machine Learning for CAD, pages 35--41, 2022."},{"key":"e_1_3_2_1_15_1","first-page":"1","volume-title":"2023 60th ACM\/IEEE Design Automation Conference (DAC)","author":"Minjeong","year":"2023","unstructured":"Minjeong Choi et al. Reinforcement learning-based analog circuit optimizer using gm\/id for sizing. In 2023 60th ACM\/IEEE Design Automation Conference (DAC), pages 1--6. IEEE, 2023."},{"key":"e_1_3_2_1_16_1","first-page":"1","volume-title":"2023 60th ACM\/IEEE Design Automation Conference (DAC)","author":"Jinxin","year":"2023","unstructured":"Jinxin Zhang et al. Automated design of complex analog circuits with multiagent based reinforcement learning. In 2023 60th ACM\/IEEE Design Automation Conference (DAC), pages 1--6. IEEE, 2023."},{"key":"e_1_3_2_1_17_1","first-page":"1219","volume-title":"2021 58th ACM\/IEEE Design Automation Conference (DAC)","author":"Ahmet","year":"2021","unstructured":"Ahmet Budak et al. Dnn-Opt: An rl inspired optimization for analog circuit sizing using deep neural networks. In 2021 58th ACM\/IEEE Design Automation Conference (DAC), pages 1219--1224. IEEE, 2021."},{"key":"e_1_3_2_1_18_1","first-page":"1","volume-title":"Automation Test in Europe Conference Exhibition (DATE)","author":"Youngchang","year":"2023","unstructured":"Youngchang Choi et al. MA-Opt: Reinforcement learning-based analog circuit optimization using multi-actors. In 2023 Design, Automation Test in Europe Conference Exhibition (DATE), pages 1--5, 2023."},{"key":"e_1_3_2_1_19_1","volume-title":"Deep reinforcement learning in a handful of trials using probabilistic dynamics models. Advances in neural information processing systems, 31","author":"Kurtland Chua","year":"2018","unstructured":"Kurtland Chua et al. Deep reinforcement learning in a handful of trials using probabilistic dynamics models. Advances in neural information processing systems, 31, 2018."},{"key":"e_1_3_2_1_20_1","volume-title":"When to trust your model: Model-based policy optimization. Advances in neural information processing systems, 32","author":"Michael Janner","year":"2019","unstructured":"Michael Janner et al. When to trust your model: Model-based policy optimization. Advances in neural information processing systems, 32, 2019."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2006.884077"}],"event":{"name":"DAC '24: 61st ACM\/IEEE Design Automation Conference","location":"San Francisco CA USA","acronym":"DAC '24","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE-CEDA","SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 61st ACM\/IEEE Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649329.3657335","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3649329.3657335","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:00Z","timestamp":1750295880000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649329.3657335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,23]]},"references-count":21,"alternative-id":["10.1145\/3649329.3657335","10.1145\/3649329"],"URL":"https:\/\/doi.org\/10.1145\/3649329.3657335","relation":{},"subject":[],"published":{"date-parts":[[2024,6,23]]},"assertion":[{"value":"2024-11-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}