4 citations · 5 across the 5 of their papers we have counts for
5 papers
On the continuity and smoothness of the value function in reinforcement learning and optimal control
Hans Harder, Sebastian Peitz
The value function plays a crucial role as a measure for the cumulative future reward an agent receives in both reinforcement learning and optimal control. It is therefore of inter…
A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces
Konstantin Sonntag, Bennet Gebken, Georg Müller +2
The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] is extended from finite-dimensional problems to general Hilbert spa…
Multiobjective Optimization of Non-Smooth PDE-Constrained Problems
Marco Bernreuther, Michael Dellnitz, Bennet Gebken +4
Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimizati…
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs
Stefan Werner, Sebastian Peitz
The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by part…
Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-like Systems
Konstantin Sonntag, Sebastian Peitz
We derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To th…