6 papers
Certified Neural Approximations of Nonlinear Dynamics
Frederik Baymler Mathiesen, Nikolaus Vertovec, Francesco Fabiano +2
Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such…
Robust Shielding for Safe Reinforcement Learning
Edwin Hamel-De le Court, Thom Badings, Alessandro Abate +2
Shielding is an effective approach to formally guarantee the safety of reinforcement learning agents in Markov decision processes (MDPs). However, existing shielding techniques typ…
Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
Nikolaus Vertovec, Frederik Baymler Mathiesen, Thom Badings +2
Control barrier functions (CBFs) are a popular tool for safety certification of nonlinear dynamical control systems. Recently, CBFs represented as neural networks have shown great…
Temporal Logic Control of Nonlinear Stochastic Systems with Online Performance Optimization
Alessandro Riccardi, Thom Badings, Luca Laurenti +2
The deployment of autonomous systems in safety-critical environments requires control policies that guarantee satisfaction of complex control specifications. These systems are comm…
Best-Effort Policies for Robust Markov Decision Processes
Alessandro Abate, Thom Badings, Giuseppe De Giacomo +1
We study the common generalization of Markov decision processes (MDPs) with sets of transition probabilities, known as robust MDPs (RMDPs). A standard goal in RMDPs is to compute a…
SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RL
Jacques Cloete, Nikolaus Vertovec, Alessandro Abate
To apply reinforcement learning to safety-critical applications, we ought to provide safety guarantees during both policy training and deployment. In this work, we present theoreti…