{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:24:24Z","timestamp":1743031464462,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031486418"},{"type":"electronic","value":"9783031486425"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-48642-5_1","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T09:02:16Z","timestamp":1700902936000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reinforcement Learning Model in\u00a0Automated Greenhouse Control"],"prefix":"10.1007","author":[{"given":"F. Javier","family":"Ferr\u00e1ndez-Pastor","sequence":"first","affiliation":[]},{"given":"Jos\u00e9 M.","family":"C\u00e1mara-Zapata","sequence":"additional","affiliation":[]},{"given":"Sara","family":"Alca\u00f1iz-Lucas","sequence":"additional","affiliation":[]},{"given":"Sof\u00eda","family":"Pardo","sequence":"additional","affiliation":[]},{"given":"Jose A.","family":"Brenes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"issue":"5","key":"1_CR1","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.tics.2019.02.006","volume":"23","author":"M Botvinick","year":"2019","unstructured":"Botvinick, M., Ritter, S., Wang, J.X., Kurth-Nelson, Z., Blundell, C., Hassabis, D.: Reinforcement learning, fast and slow. Trends Cogn. Sci. 23(5), 408\u2013422 (2019). https:\/\/doi.org\/10.1016\/j.tics.2019.02.006. ISSN 1364\u20136613","journal-title":"Trends Cogn. Sci."},{"key":"1_CR2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1613\/jair.301","volume":"4","author":"LP Kaelbling","year":"1996","unstructured":"Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237\u2013285 (1996)","journal-title":"J. Artif. Intell. Res."},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Yang, S., Wan, M.P., Chen, W., Ng, B.F., Dubey, S.: Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Appl. Energy 271, 115147 (2020). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306261920306590","DOI":"10.1016\/j.apenergy.2020.115147"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Drgona, J., Picard, D., Kvasnica, M., Helsen, L.: Approximate model predictive building control via machine learning, Appl. Energy 218, 199\u2013216 (2018). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306261918302903","DOI":"10.1016\/j.apenergy.2018.02.156"},{"key":"1_CR5","volume-title":"Reinforcement Learning: An Introduction","author":"RS Sutton","year":"1999","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT press, Cambridge (1999)"},{"key":"1_CR6","volume-title":"Markov Decision Processes: Discrete Stochastic Dynamic Programming","author":"ML Puterman","year":"2005","unstructured":"Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, Hoboken (2005)"},{"key":"1_CR7","unstructured":"Brockman, G., et al.: Openai gym. ArXiv Preprint. ArXiv:1606.01540 (2016)"},{"key":"1_CR8","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.enbuild.2013.12.018","volume":"71","author":"R Missaoui","year":"2014","unstructured":"Missaoui, R., Joumaa, H., Ploix, S., Bacha, S.: Managing energy smart homes according to energy prices: analysis of a building energy management system. Energy Build. 71, 155\u2013167 (2014). https:\/\/doi.org\/10.1016\/j.enbuild.2013.12.018","journal-title":"Energy Build."},{"key":"1_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2019.104211","volume":"95","author":"ET Maddalena","year":"2020","unstructured":"Maddalena, E.T., Lian, Y., Jones, C.N.: Data-driven methods for building control\u2014a review and promising future directions. Control Eng. Pract. 95, 104211 (2020). https:\/\/doi.org\/10.1016\/j.conengprac.2019.104211","journal-title":"Control Eng. Pract."},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Chen, B., Cai, Z., Berg\u00e9s, M.: Gnu-RL: a precocial reinforcement learning solution for building HVAC control using a differentiable MPC policy. In: Proceedings of 6th ACM International Conference on System Energy-Efficient Buildings, Cities, Transports, pp. 316\u2013325 (2019)","DOI":"10.1145\/3360322.3360849"},{"issue":"5","key":"1_CR11","doi-asserted-by":"publisher","first-page":"2312","DOI":"10.1109\/TSG.2015.2396993","volume":"6","author":"Z Wen","year":"2015","unstructured":"Wen, Z., O\u2019Neill, D., Maei, H.: Optimal demand response using device-based reinforcement learning. IEEE Trans. Smart Grid 6(5), 2312\u20132324 (2015)","journal-title":"IEEE Trans. Smart Grid"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Wei, T., Wang, Y., Zhu, Q.: Deep reinforcement learning for building HVAC control. In: Proceedings of 54th Annual Design Automation Conference, pp. 1\u20136 (2017)","DOI":"10.1145\/3061639.3062224"},{"key":"1_CR13","unstructured":"Mankowitz, D., Hester, T.: Challenges of real-world reinforcement learning. ArXiv arxiv:1904.12901 (2019)"},{"key":"1_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.apenergy.2021.118346","volume":"309","author":"J Arroyo","year":"2022","unstructured":"Arroyo, J., Manna, C., Spiessens, F., Helsen, L.: Reinforced model predictive control (RL-MPC) for building energy management. Appl. Energy 309, 1 (2022). https:\/\/doi.org\/10.1016\/j.apenergy.2021.118346","journal-title":"Appl. Energy"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Morcego, B., Yin, W., Boersma, S., van Henten, E., Puig, V., Sun, C.: Reinforcement learning versus model predictive control on greenhouse climate control. arXiv preprint arXiv:2303.06110 (2023)","DOI":"10.2139\/ssrn.4525429"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Gillies, M., Fiebrink, R., Tanaka, A.: Human-Centred machine learning. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery, New York (2016)","DOI":"10.1145\/2851581.2856492"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the 15th International Conference on Ubiquitous Computing &amp; Ambient Intelligence (UCAmI 2023)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-48642-5_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T09:12:41Z","timestamp":1700903561000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-48642-5_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031486418","9783031486425"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-48642-5_1","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UCAmI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Ubiquitous Computing and Ambient Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Riviera Maya","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ucami2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ucami.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}