{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T21:25:24Z","timestamp":1774387524647,"version":"3.50.1"},"reference-count":29,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quality &amp;amp; Reliability Eng"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accelerated life tests (ALTs) are essential tools for estimating product reliability under high\u2010stress conditions, allowing failure data to be collected in reduced timeframes. However, planning effective ALT configurations is a complex task that requires selecting stress levels, test durations, and unit allocations while accounting for limited resources and model uncertainties. This study proposes a reinforcement learning (RL) approach to automate and optimize test planning under a failure model that emulates real\u2010world reliability patterns. The test environment simulates product failures using a mixture of Weibull distributions modulated by an Arrhenius temperature scaling, yielding possibly different hazard functions, such as the power\u2010law increasing and decreasing curves, constant, and the bathtub\u2010shaped curve, typical in industrial applications. A Double Deep Q\u2010Network (DDQN) agent is trained to sequentially configure test levels by selecting temperature, time, and sample size, with the goal of minimizing the statistical uncertainty in estimating the thermal sensitivity parameter , which is crucial for extrapolating lifespan predictions under normal operating conditions. The trained agent consistently produced shorter test plans that enabled precise estimation of all model parameters, notably achieving lower variance in the  estimate compared to competing methods. These results suggest that the proposed RL framework offers a flexible and adaptive alternative to traditional experimental\u00a0designs.<\/jats:p>","DOI":"10.1002\/qre.70134","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T10:24:45Z","timestamp":1765967085000},"page":"980-991","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimal Accelerated Life Testing Design Under Constrained Resources Using Double Deep Q\u2010Learning"],"prefix":"10.1002","volume":"42","author":[{"given":"Allan Jonathan","family":"da Silva","sequence":"first","affiliation":[{"name":"Coordination of Applied Mathematics and Computing National Laboratory for Scientific Computing (LNCC) Petropolis Rio de Janeiro Brazil"},{"name":"Department of Production Engineering Federal Center for Technological Education Celso Suckow da Fonseca (Cefet\/RJ) Itaguai Rio de Janeiro Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos A. A.","family":"Gomes","sequence":"additional","affiliation":[{"name":"Department of Production Engineering Federal Center for Technological Education Celso Suckow da Fonseca (Cefet\/RJ) Itaguai Rio de Janeiro Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucas R.","family":"de Souza","sequence":"additional","affiliation":[{"name":"Department of Production Engineering Federal Center for Technological Education Celso Suckow da Fonseca (Cefet\/RJ) Itaguai Rio de Janeiro Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rhenan S. dos","family":"Santos","sequence":"additional","affiliation":[{"name":"Department of Production Engineering Federal Center for Technological Education Celso Suckow da Fonseca (Cefet\/RJ) Itaguai Rio de Janeiro Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TR.1980.5220742"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.110222"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s10033-018-0206-9"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113701"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12190"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2024.04.003"},{"issue":"4","key":"e_1_2_9_9_1","first-page":"1014","article-title":"Impact of Product Quality on Customer Satisfaction: Evidence From Selected Consumer Durables","volume":"8","author":"Lone R.","year":"2023","journal-title":"International Journal for Research Trends and Innovation"},{"key":"e_1_2_9_10_1","series-title":"Wiley Series in Probability and Statistics","volume-title":"Accelerated Testing: Statistical Models, Test Plans, and Data Analysis","author":"Nelson W.","year":"2009"},{"key":"e_1_2_9_11_1","volume-title":"Handbook of Reliability Engineering","author":"Pham H.","year":"2006"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1998.10485191"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14146198"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.polymdegradstab.2023.110281"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1214\/088342306000000321"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.2296"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2024.3358233"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2006.890885"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.2174\/1872212113666191209150647"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008202821328"},{"key":"e_1_2_9_21_1","doi-asserted-by":"crossref","unstructured":"H.Van Hasselt A.Guez andD.Silver \u201cDeep Reinforcement Learning With Double Q\u2010Learning \u201d inProceedings of the AAAI Conference on Artificial Intelligence vol.30(2016).","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"e_1_2_9_22_1","volume-title":"Reinforcement Learning: An Introduction","author":"Sutton R.","year":"2018"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1959.tb00338.x"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-009-5912-5"},{"key":"e_1_2_9_25_1","volume-title":"Optimal Design of Experiments","author":"Pukelsheim F.","year":"1993"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198522546.001.0001"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780199296590.001.0001"},{"key":"e_1_2_9_28_1","volume-title":"Theory of Optimal Experiments","author":"Fedorov V. 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