{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:25:49Z","timestamp":1782174349096,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":15,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,17]]},"DOI":"10.1145\/3679240.3734671","type":"proceedings-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T13:13:42Z","timestamp":1750079622000},"page":"968-972","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0785-4028","authenticated-orcid":false,"given":"Toon","family":"Van Puyvelde","sequence":"first","affiliation":[{"name":"IDLab, Ghent University - imec, Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0695-7682","authenticated-orcid":false,"given":"Mehran","family":"Zareh","sequence":"additional","affiliation":[{"name":"Daikin Europe NV, Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2707-4176","authenticated-orcid":false,"given":"Chris","family":"Develder","sequence":"additional","affiliation":[{"name":"IDLab, Ghent University - imec, Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"e_1_3_3_2_2_2","first-page":"175","volume-title":"Proceedings of Building Simulation 2021: 17th Conference of IBPSA","volume":"17","author":"Arroyo Javier","year":"2022","unstructured":"Javier Arroyo, Carlo Manna, Fred Spiessens, Lieve Helsen, D Saelens, J Laverge, W Boydens, and L Helsen. 2022. An OpenAI-gym environment for the building optimization testing (BOPTEST) framework. In Proceedings of Building Simulation 2021: 17th Conference of IBPSA, Vol.\u00a017. INT BUILDING PERFORMANCE SIMULATION ASSOC-IBPSA, 175\u2013182."},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470"},{"key":"e_1_3_3_2_4_2","first-page":"1","volume-title":"Proceedings of the IJCAI 2019 workshop on explainable artificial intelligence","author":"Coppens Youri","year":"2019","unstructured":"Youri Coppens, Kyriakos Efthymiadis, Tom Lenaerts, Ann Now\u00e9, Tim Miller, Rosina Weber, and Daniele Magazzeni. 2019. Distilling deep reinforcement learning policies in soft decision trees. In Proceedings of the IJCAI 2019 workshop on explainable artificial intelligence. 1\u20136."},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Rudresh Dwivedi Devam Dave Het Naik Smiti Singhal Rana Omer Pankesh Patel Bin Qian Zhenyu Wen Tejal Shah Graham Morgan et\u00a0al. 2023. Explainable AI (XAI): Core ideas techniques and solutions. Comput. Surveys 55 9 (2023) 1\u201333.","DOI":"10.1145\/3561048"},{"key":"e_1_3_3_2_6_2","unstructured":"Nicholas Frosst and Geoffrey Hinton. 2017. Distilling a neural network into a soft decision tree. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1711.09784 (2017)."},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3632775.3661937"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Vikas Hassija Vinay Chamola Atmesh Mahapatra Abhinandan Singal Divyansh Goel Kaizhu Huang Simone Scardapane Indro Spinelli Mufti Mahmud and Amir Hussain. 2024. Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation 16 1 (2024) 45\u201374.","DOI":"10.1007\/s12559-023-10179-8"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Alexandre Heuillet Fabien Couthouis and Natalia D\u00edaz-Rodr\u00edguez. 2022. Collective explainable AI: Explaining cooperative strategies and agent contribution in multiagent reinforcement learning with shapley values. IEEE Computational Intelligence Magazine 17 1 (2022) 59\u201371.","DOI":"10.1109\/MCI.2021.3129959"},{"key":"e_1_3_3_2_10_2","unstructured":"Yacine Izza Alexey Ignatiev and Joao Marques-Silva. 2020. On explaining decision trees. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2010.11034 (2020)."},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Seyed\u00a0Soroush Karimi\u00a0Madahi Bert Claessens and Chris Develder. 2024. Distributional reinforcement learning-based energy arbitrage strategies in imbalance settlement mechanism. J. Energy Storage 104 Part A (Dec. 2024) 114377.","DOI":"10.1016\/j.est.2024.114377"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9377735"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Xiao-Hui Li Caleb\u00a0Chen Cao Yuhan Shi Wei Bai Han Gao Luyu Qiu Cong Wang Yuanyuan Gao Shenjia Zhang Xun Xue et\u00a0al. 2020. A survey of data-driven and knowledge-aware explainable AI. IEEE Transactions on Knowledge and Data Engineering 34 1 (2020) 29\u201349.","DOI":"10.1109\/TKDE.2020.2983930"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Stephanie Milani Nicholay Topin Manuela Veloso and Fei Fang. 2024. Explainable reinforcement learning: A survey and comparative review. Comput. Surveys 56 7 (2024) 1\u201336.","DOI":"10.1145\/3616864"},{"key":"e_1_3_3_2_15_2","first-page":"1855","volume-title":"International conference on artificial intelligence and statistics","author":"Silva Andrew","year":"2020","unstructured":"Andrew Silva, Matthew Gombolay, Taylor Killian, Ivan Jimenez, and Sung-Hyun Son. 2020. Optimization methods for interpretable differentiable decision trees applied to reinforcement learning. In International conference on artificial intelligence and statistics. PMLR, 1855\u20131865."},{"key":"e_1_3_3_2_16_2","first-page":"226","volume-title":"Advances in Neural Information Processing Systems","author":"Tseng Wei-Cheng","year":"2022","unstructured":"Wei-Cheng Tseng, Tsun-Hsuan\u00a0Johnson Wang, Yen-Chen Lin, and Phillip Isola. 2022. Offline Multi-Agent Reinforcement Learning with Knowledge Distillation. In Advances in Neural Information Processing Systems, S.\u00a0Koyejo, S.\u00a0Mohamed, A.\u00a0Agarwal, D.\u00a0Belgrave, K.\u00a0Cho, and A.\u00a0Oh (Eds.), Vol.\u00a035. Curran Associates, Inc., 226\u2013237."}],"event":{"name":"E-Energy '25: The 16th ACM International Conference on Future and Sustainable Energy Systems","location":"Rotterdam Netherlands","acronym":"E-Energy '25","sponsor":["SIGEnergy ACM Special Interest Group on Energy Systems and Informatics"]},"container-title":["Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3679240.3734671","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T14:01:43Z","timestamp":1750082503000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3679240.3734671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,16]]},"references-count":15,"alternative-id":["10.1145\/3679240.3734671","10.1145\/3679240"],"URL":"https:\/\/doi.org\/10.1145\/3679240.3734671","relation":{},"subject":[],"published":{"date-parts":[[2025,6,16]]},"assertion":[{"value":"2025-06-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}