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- W4386736705 abstract "This paper formalizes a constrained maximum-entropy Linear Quadratic Regulator (LQR) problem and provides a computationally-efficient solution for it. The traditional LQR cost is penalized with an additional entropy cost, and a cumulative safety constraint is also imposed. This formulation will not only promote randomness to achieve exploration or unpredictability but also ensure that the performance is deemed satisfactory despite randomness. A stochastic controller is designed, and it is first shown that the resulting optimization problem for finding the control gain and covariance is non-convex. By employing Lagrange duality formalism, the non-convex optimization problem is then transformed into a convex one. More specifically, the constrained entropy-regularized LQR problem is reformulated as a set of linear matrix inequalities (LMIs), facilitating the determination of the control gain and covariance. A simulation example is provided to verify the effectiveness of the presented results." @default.
- W4386736705 created "2023-09-15" @default.
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- W4386736705 date "2023-01-01" @default.
- W4386736705 modified "2023-10-06" @default.
- W4386736705 title "Maximum-Entropy Satisficing Linear Quadratic Regulator" @default.
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- W4386736705 doi "https://doi.org/10.1109/lcsys.2023.3315651" @default.
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