We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function's parameters.
Sep 27, 2019
We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable ...
We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable ...
Nov 1, 2024 · We show how to use CEM to solve a pendulum control problem, which can be made differentiable by setting a non-zero temperature for the soft top ...
We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable ...
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Nov 21, 2019 · This paper proposes to unroll the cross entropy method, which is very different than the standard practice of unrolling gradient descent.
We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable ...
Dec 8, 2022 · The algorithm uses a Model Predictive Control (MPC) framework with a differentiable cross-entropy optimizer, which induces a differentiable ...
We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable.
Method: The differentiable-cross entropy method. Applications. Learning deep ... Method: The differentiable-cross entropy method. Applications. Learning ...