3 citations · 3 across the 14 of their papers we have counts for
4 papers · 1 filter
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis
Rohan Mitta, Hosein Hasanbeig, Jun Wang +3
This paper addresses the problem of maintaining safety during training in Reinforcement Learning (RL), such that the safety constraint violations are bounded at any point during le…
Learning Robust Policies for Uncertain Parametric Markov Decision Processes
Luke Rickard, Alessandro Abate, Kostas Margellos
Synthesising verifiably correct controllers for dynamical systems is crucial for safety-critical problems. To achieve this, it is important to account for uncertainty in a robust m…
Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractions
Thom Badings, Nils Jansen, Licio Romao +1
Automated synthesis of correct-by-construction controllers for autonomous systems is crucial for their deployment in safety-critical scenarios. Such autonomous systems are naturall…
Probabilistic Reach-Avoid for Bayesian Neural Networks
Matthew Wicker, Luca Laurenti, Andrea Patane +3
Model-based reinforcement learning seeks to simultaneously learn the dynamics of an unknown stochastic environment and synthesise an optimal policy for acting in it. Ensuring the s…