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Reinforcement Learning for Multi-Purpose Schedules Host Publication: 5th International Conference on Agents and Artificial Intelligence (ICAART 2013) Authors: K. Marguerite Van Moffaert, Y. De Hauwere, P. Vrancx and A. Nowé Publisher: Scitepress Publication Date: Feb. 2013 Number of Pages: 7 ISBN: 978-989-8565-39-6
Abstract: In this paper, we present a learning technique for determining schedules for general devices that focus on a combination of two objectives. These objectives are user-convenience and gains in energy savings. The proposed learning algorithm is based on Fitted-Q Iteration (FQI) and analyzes the usage and the users of a particular device to decide upon the appropriate profile of start-up and shutdown times of that equipment. The algorithm is experimentally evaluated on real-life data to discover that close-to-optimal control policies can be learned on a short timespan of a only few iterations. Our results show that the algorithm is capable of proposing intelligent schedules depending on which objective the user placed more or less emphasis on.
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