7 citations · 12 across the 6 of their papers we have counts for
5 papers · 1 filter
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
Dominik Straub, Matthias Schultheis, Heinz Koeppl +1
Inverse optimal control can be used to characterize behavior in sequential decision-making tasks. Most existing work, however, is limited to fully observable or linear systems, or…
Reinforcement Learning with Non-Exponential Discounting
Matthias Schultheis, Constantin A. Rothkopf, Heinz Koeppl
Commonly in reinforcement learning (RL), rewards are discounted over time using an exponential function to model time preference, thereby bounding the expected long-term reward. In…
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System
Matthias Schultheis, Dominik Straub, Constantin A. Rothkopf
Computational level explanations based on optimal feedback control with signal-dependent noise have been able to account for a vast array of phenomena in human sensorimotor behavio…
POMDPs in Continuous Time and Discrete Spaces
Bastian Alt, Matthias Schultheis, Heinz Koeppl
Many processes, such as discrete event systems in engineering or population dynamics in biology, evolve in discrete space and continuous time. We consider the problem of optimal de…
Receding Horizon Curiosity
Matthias Schultheis, Boris Belousov, Hany Abdulsamad +1
Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion. A principled treatme…