59 citations · 291 across the 45 of their papers we have counts for
10 papers · 1 filter
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…
Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics
Luke Rickard, Thom Badings, Licio Romao +1
Automated synthesis of provably correct controllers for cyber-physical systems is crucial for deployment in safety-critical scenarios. However, hybrid features and stochastic or un…
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…
Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty
Thom Badings, Licio Romao, Alessandro Abate +1
Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of…
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…
Parameter Synthesis in Markov Models: A Gentle Survey
Nils Jansen, Sebastian Junges, Joost-Pieter Katoen
This paper surveys the analysis of parametric Markov models whose transitions are labelled with functions over a finite set of parameters. These models are symbolic representations…
Sampling-Based Verification of CTMCs with Uncertain Rates
Thom S. Badings, Nils Jansen, Sebastian Junges +2
We employ uncertain parametric CTMCs with parametric transition rates and a prior on the parameter values. The prior encodes uncertainty about the actual transition rates, while th…
Robust Anytime Learning of Markov Decision Processes
Marnix Suilen, Thiago D. Simão, David Parker +1
Markov decision processes (MDPs) are formal models commonly used in sequential decision-making. MDPs capture the stochasticity that may arise, for instance, from imprecise actuator…
Safe Reinforcement Learning via Shielding under Partial Observability
Steven Carr, Nils Jansen, Sebastian Junges +1
Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment. A family of appr…