4 papers
Policy Iteration for Linear-Quadratic Stochastic Differential Games with State- and Control-Dependent Noise
Karl Handwerker, Felix Thömmes, Lucas Günther +2
This paper presents a novel sequential policy iteration (PI) method for stochastic differential games with state- and control-dependent noise. The updates preserve mean-square stab…
Infinite-Horizon Inverse Linear-Quadratic Differential Games with State- and Control-Dependent Noise
Lucas Günther, Karl Handwerker, Felix Thömmes +2
This paper presents a method to solve the inverse problem for N-player infinite-horizon linear-quadratic (LQ) differential games with state- and control-dependent noise. For this s…
Data-Driven Continuous-Time Linear Quadratic Regulator via Closed-Loop and Reinforcement Learning Parameterizations
Armin Gießler, Felix Thömmes, Sören Hohmann
This paper studies data-driven approaches to the continuous-time linear quadratic regulator (LQR) problem based on two existing parameterizations, namely a closed-loop (CL) paramet…
Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators
Konstantin Lübeck, Alexander Louis-Ferdinand Jung, Felix Wedlich +8
Implementing Deep Neural Networks (DNNs) on resource-constrained edge devices is a challenging task that requires tailored hardware accelerator architectures and a clear understand…