Spotlight
Optimizing Energy Production Using Policy Search and Predictive State Representations
Yuri Grinberg · Doina Precup · Michel Gendreau

Wed Dec 10th 03:30 -- 03:50 PM @ Level 2, room 210
in Spotlight Session 7: Reinforcement learning spotlights »

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)

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