Task-based End-to-end Model Learning in Stochastic Optimization
نویسندگان
چکیده
In practice, we use SQP to solve (*), finding z⋆ x; θ via a solution for fast argmin differentiation in QPs [3] and then taking derivatives through the quadratic approximation at this optimum. Technical Challenge: Argmin Differentiation We outperform both traditional model learning and model-free policy optimization in terms of task cost, the objective of actual interest in the closed-loop system.
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