Steffen Udluft

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Siemens AG

Head of research team Learning Systems, Researcher Reinforcement Learning, Corporate Technology Munich at Siemens

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Behavior Constraining in Weight Space for Offline Reinforcement Learning

In offline reinforcement learning, a policy needs to be learned from a s...
07/12/2021 ∙ by Phillip Swazinna, et al. ∙ 0 ∙ share

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Overcoming Model Bias for Robust Offline Deep Reinforcement Learning

State-of-the-art reinforcement learning algorithms mostly rely on being ...
08/12/2020 ∙ by Phillip Swazinna, et al. ∙ 0 ∙ share

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Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies,...
04/29/2018 ∙ by Daniel Hein, et al. ∙ 0 ∙ share

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Interpretable Policies for Reinforcement Learning by Genetic Programming

The search for interpretable reinforcement learning policies is of high ...
12/12/2017 ∙ by Daniel Hein, et al. ∙ 0 ∙ share

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Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks

We derive a novel sensitivity analysis of input variables for predictive...
12/10/2017 ∙ by Stefan Depeweg, et al. ∙ 0 ∙ share

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

Bayesian neural networks (BNNs) with latent variables are probabilistic ...
10/19/2017 ∙ by Stefan Depeweg, et al. ∙ 0 ∙ share

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A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel an...
09/27/2017 ∙ by Daniel Hein, et al. ∙ 0 ∙ share

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Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables

Bayesian neural networks (BNNs) with latent variables are probabilistic ...
06/26/2017 ∙ by Stefan Depeweg, et al. ∙ 0 ∙ share

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Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

The Particle Swarm Optimization Policy (PSO-P) has been recently introdu...
05/20/2017 ∙ by Daniel Hein, et al. ∙ 0 ∙ share

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Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies

Fuzzy controllers are efficient and interpretable system controllers for...
10/19/2016 ∙ by Daniel Hein, et al. ∙ 0 ∙ share

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

We present an algorithm for model-based reinforcement learning that comb...
05/23/2016 ∙ by Stefan Depeweg, et al. ∙ 0 ∙ share

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