{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T15:24:40Z","timestamp":1784906680042,"version":"3.55.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>In the context of urgent climate challenges and the pressing need for rapid technology development, Reinforcement Learning (RL) stands as a compelling data-driven method for controlling real-world physical systems. However, RL implementation often entails time-consuming and computationally intensive data collection and training processes, rendering them inefficient for real-time applications that lack non-real-time models. To address these limitations, real-time emulation techniques have emerged as valuable tools for the lab-scale rapid prototyping of intricate energy systems. While emulated systems offer a bridge between simulation and reality, they too face constraints, hindering comprehensive characterization, testing, and development. In this research, we construct a surrogate model using limited data from simulated systems, enabling an efficient and effective training process for a Double Deep Q-Network (DDQN) agent for future deployment. Our approach is illustrated through a hydropower application, demonstrating the practical impact of our approach on climate-related technology development.<\/jats:p>","DOI":"10.1609\/aaaiss.v2i1.27663","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:50:04Z","timestamp":1705971004000},"page":"153-158","source":"Crossref","is-referenced-by-count":2,"title":["Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments"],"prefix":"10.1609","volume":"2","author":[{"given":"Alexander","family":"Stevenson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayank","family":"Panwar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rob","family":"Hovsapian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arif","family":"Sarwat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9382","published-online":{"date-parts":[[2024,1,22]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/27663\/27436","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/27663\/27436","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:50:04Z","timestamp":1705971004000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/27663"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1,22]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v2i1.27663","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,22]]}}}