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We consider the challenging practical problem of optimizing the power production of a complex of hydroelectric power plants, which involves control over three continuous action variables, uncertainty in the amount of water inflows and a variety of constraints that need to be satisfied. We propose a policy-search-based approach coupled with predictive modelling to address this problem. This approach has some key advantages compared to other alternatives, such as dynamic programming: the policy representation and search algorithm can conveniently incorporate domain knowledge; the resulting policies are easy to interpret, and the algorithm is naturally parallelizable. Our algorithm obtains a policy which outperforms the solution found by dynamic programming both quantitatively and qualitatively.
Author Information
Yuri Grinberg (McGill University)
Doina Precup (McGill University / Mila / DeepMind Montreal)
Michel Gendreau (CIRRELT)
Related Events (a corresponding poster, oral, or spotlight)
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2014 Poster: Optimizing Energy Production Using Policy Search and Predictive State Representations »
Thu Dec 11th 12:00 -- 04:59 AM Room Level 2, room 210D
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