{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/convolutional-neural-networks-for-automatic-1","title":"Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control","arxiv_id":"1604.08382","date":"2016-04-28","proceeding":null,"authors":["Bert J. Claessens","Peter Vrancx","Frederik Ruelens"],"abstract":"Direct load control of a heterogeneous cluster of residential demand\nflexibility sources is a high-dimensional control problem with partial\nobservability. This work proposes a novel approach that uses a convolutional\nneural network to extract hidden state-time features to mitigate the curse of\npartial observability. More specific, a convolutional neural network is used as\na function approximator to estimate the state-action value function or\nQ-function in the supervised learning step of fitted Q-iteration. The approach\nis evaluated in a qualitative simulation, comprising a cluster of\nthermostatically controlled loads that only share their air temperature, whilst\ntheir envelope temperature remains hidden. The simulation results show that the\npresented approach is able to capture the underlying hidden features and\nsuccessfully reduce the electricity cost the cluster.","url_abs":"http://arxiv.org/abs/1604.08382v2","url_pdf":"http://arxiv.org/pdf/1604.08382v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"convolutional-neural-networks-for-automatic-1","repo_url":"https://github.com/tahanakabi/Deep-Reinforcenment-learning-for-TCL-control","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}