7 citations · 19 across the 11 of their papers we have counts for
12 papers
Compositional shield synthesis for safe reinforcement learning in partial observability
Steven Carr, Georgios Bakirtzis, Ufuk Topcu
Agents controlled by the output of reinforcement learning (RL) algorithms often transition to unsafe states, particularly in uncertain and partially observable environments. Partia…
Adaptive planning for risk-aware predictive digital twins
Marco Tezzele, Steven Carr, Ufuk Topcu +1
This work proposes a mathematical framework to increase the robustness to rare events of digital twins modelled with graphical models. We incorporate probabilistic model-checking a…
Pessimistic Iterative Planning with RNNs for Robust POMDPs
Maris F. L. Galesloot, Marnix Suilen, Thiago D. Simão +4
Robust POMDPs extend classical POMDPs to incorporate model uncertainty using so-called uncertainty sets on the transition and observation functions, effectively defining ranges of…
Formal Methods for Autonomous Systems
Tichakorn Wongpiromsarn, Mahsa Ghasemi, Murat Cubuktepe +6
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems. The main buil…
Fine-Tuning Language Models Using Formal Methods Feedback
Yunhao Yang, Neel P. Bhatt, Tyler Ingebrand +4
Although pre-trained language models encode generic knowledge beneficial for planning and control, they may fail to generate appropriate control policies for domain-specific tasks.…
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…