Steffen Udluft | DeepAI

DeepAI

Featured Co-authors

Finale Doshi-Velez
96 publications
José Miguel Hernández-Lobato
82 publications
Thomas Runkler
26 publications
Felix Dietrich
18 publications
Simon Wiedemann
11 publications
Daniel Hein
10 publications
Thomas A. Runkler
7 publications
Alexander Hentschel
6 publications
Stefan Depeweg
6 publications
Clemens Otte
5 publications
Phillip Swazinna
5 publications

∙ 08/01/2022

Safe Policy Improvement Approaches and their Limitations

Safe Policy Improvement (SPI) is an important technique for offline rein...

Philipp Scholl, et al. ∙

∙ 06/09/2022

Quantum Policy Iteration via Amplitude Estimation and Grover Search – Towards Quantum Advantage for Reinforcement Learning

We present a full implementation and simulation of a novel quantum reinf...

Simon Wiedemann, et al. ∙

∙ 05/21/2022

User-Interactive Offline Reinforcement Learning

Offline reinforcement learning algorithms still lack trust in practice d...

Phillip Swazinna, et al. ∙

∙ 01/28/2022

Safe Policy Improvement Approaches on Discrete Markov Decision Processes

Safe Policy Improvement (SPI) aims at provable guarantees that a learned...

Philipp Scholl, et al. ∙

∙ 01/14/2022

Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning

Offline reinforcement learning (RL) Algorithms are often designed with e...

Phillip Swazinna, et al. ∙

∙ 11/26/2021

Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning

Recently developed offline reinforcement learning algorithms have made i...

Phillip Swazinna, et al. ∙

∙ 07/12/2021

Behavior Constraining in Weight Space for Offline Reinforcement Learning

In offline reinforcement learning, a policy needs to be learned from a s...

Phillip Swazinna, et al. ∙

∙ 08/12/2020

Overcoming Model Bias for Robust Offline Deep Reinforcement Learning

State-of-the-art reinforcement learning algorithms mostly rely on being ...

Phillip Swazinna, et al. ∙

∙ 04/29/2018

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies,...

Daniel Hein, et al. ∙

∙ 12/12/2017

Interpretable Policies for Reinforcement Learning by Genetic Programming

The search for interpretable reinforcement learning policies is of high ...

Daniel Hein, et al. ∙

∙ 12/10/2017

Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks

We derive a novel sensitivity analysis of input variables for predictive...

Stefan Depeweg, et al. ∙

∙ 10/19/2017

Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems

Bayesian neural networks (BNNs) with latent variables are probabilistic ...

Stefan Depeweg, et al. ∙

∙ 09/27/2017

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel an...

Daniel Hein, et al. ∙

∙ 06/26/2017

Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables

Bayesian neural networks (BNNs) with latent variables are probabilistic ...

Stefan Depeweg, et al. ∙

∙ 05/20/2017

Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

The Particle Swarm Optimization Policy (PSO-P) has been recently introdu...

Daniel Hein, et al. ∙

∙ 10/19/2016

Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies

Fuzzy controllers are efficient and interpretable system controllers for...

Daniel Hein, et al. ∙

∙ 05/23/2016

Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

We present an algorithm for model-based reinforcement learning that comb...

Stefan Depeweg, et al. ∙