4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.AI2016★ 1 cited
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning
Wendelin Böhmer, Rong Guo, Klaus Obermayer
This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning. We show empirically that non-deterministic pol…
cs.LG2014
Regression with Linear Factored Functions
Wendelin Böhmer, Klaus Obermayer
Many applications that use empirically estimated functions face a curse of dimensionality, because the integrals over most function classes must be approximated by sampling. This p…
cs.AI2012★ 4 cited
Robot Navigation using Reinforcement Learning and Slow Feature Analysis
Wendelin Böhmer
The application of reinforcement learning algorithms onto real life problems always bears the challenge of filtering the environmental state out of raw sensor readings. While most…