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- W2574274539 abstract "The simplicity of Approximate Dynamic Programming offers benefits for large-scale systems compared to other synthesis and control methodologies. A common technique to approximate the Dynamic Program, is through the solution of the corresponding Linear Program. The major drawback of this approach is that the online performance is very sensitive to the choice of tuning parameters, in particular the state relevance weighting parameter. Our work aims at alleviating this sensitivity. To achieve this, we propose to find a set of approximate Q-functions, each for a different choice of the tuning parameters, and then to use the pointwise maximum of the set of Q-functions for the online policy. The pointwise maximum promises to be better than using only one of individual Q-functions for the online policy. We demonstrate that this approach immunizes against tuning errors through a stylized portfolio optimization problem." @default.
- W2574274539 created "2017-01-26" @default.
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- W2574274539 date "2016-06-01" @default.
- W2574274539 modified "2023-09-30" @default.
- W2574274539 title "Alleviating tuning sensitivity in Approximate Dynamic Programming" @default.
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- W2574274539 doi "https://doi.org/10.1109/ecc.2016.7810522" @default.
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