59 citations · 291 across the 45 of their papers we have counts for
8 papers · 1 filter
Reinforcement Learning by Guided Safe Exploration
Qisong Yang, Thiago D. Simão, Nils Jansen +2
Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them…
More for Less: Safe Policy Improvement With Stronger Performance Guarantees
Patrick Wienhöft, Marnix Suilen, Thiago D. Simão +3
In an offline reinforcement learning setting, the safe policy improvement (SPI) problem aims to improve the performance of a behavior policy according to which sample data has been…
Efficient Sensitivity Analysis for Parametric Robust Markov Chains
Thom Badings, Sebastian Junges, Ahmadreza Marandi +2
We provide a novel method for sensitivity analysis of parametric robust Markov chains. These models incorporate parameters and sets of probability distributions to alleviate the of…
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking
Dennis Gross, Thiago D. Simao, Nils Jansen +1
Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adver…
Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation
Yannick Hogewind, Thiago D. Simao, Tal Kachman +1
We address the problem of safe reinforcement learning from pixel observations. Inherent challenges in such settings are (1) a trade-off between reward optimization and adhering to…
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model Checking
Dennis Gross, Nils Jansen, Sebastian Junges +1
This paper presents COOL-MC, a tool that integrates state-of-the-art reinforcement learning (RL) and model checking. Specifically, the tool builds upon the OpenAI gym and the proba…
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning
Zaharah A. Bukhsh, Nils Jansen, Hajo Molegraaf
Cost-effective asset management is an area of interest across several industries. Specifically, this paper develops a deep reinforcement learning (DRL) solution to automatically de…
Robustness Verification for Classifier Ensembles
Dennis Gross, Nils Jansen, Guillermo A. Pérez +1
We give a formal verification procedure that decides whether a classifier ensemble is robust against arbitrary randomized attacks. Such attacks consist of a set of deterministic at…