activity
20192022
most citedUncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning

14 citations · 44 across the 10 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.LG2020★ 4 cited

SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning

Fabian Ritz, Thomy Phan, Robert Müller +8

A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems. While such strategies sometimes yield superior performance, they m…

eess.AS2020

Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms

Steffen Illium, Robert Müller, Andreas Sedlmeier +1

In many fields of research, labeled datasets are hard to acquire. This is where data augmentation promises to overcome the lack of training data in the context of neural network en…

cs.LG2020

Policy Entropy for Out-of-Distribution Classification

Andreas Sedlmeier, Robert Müller, Steffen Illium +1

One critical prerequisite for the deployment of reinforcement learning systems in the real world is the ability to reliably detect situations on which the agent was not trained. Su…

cs.LG2020

Trajectory annotation using sequences of spatial perception

Sebastian Feld, Steffen Illium, Andreas Sedlmeier +1

In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities. In order to simplify the…

cs.LG2020

Bayesian Surprise in Indoor Environments

Sebastian Feld, Andreas Sedlmeier, Markus Friedrich +2

This paper proposes a novel method to identify unexpected structures in 2D floor plans using the concept of Bayesian Surprise. Taking into account that a person's expectation is an…