Model-Free Learning of Safe yet Effective Controllers
Robotics
2026-04-07 v2 Formal Languages and Automata Theory
Machine Learning
Logic in Computer Science
Abstract
We study the problem of learning safe control policies that are also effective; i.e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task, and the discounted reward capturing the (classic) control performance. We consider unknown environments modeled as Markov decision processes. We propose a model-free reinforcement learning algorithm that learns a policy that first maximizes the probability of ensuring safety, then the probability of satisfying the given LTL specification and lastly, the sum of discounted Quality of Control rewards. Finally, we illustrate applicability of our RL-based approach.
Cite
@article{arxiv.2103.14600,
title = {Model-Free Learning of Safe yet Effective Controllers},
author = {Alper Kamil Bozkurt and Yu Wang and Miroslav Pajic},
journal= {arXiv preprint arXiv:2103.14600},
year = {2026}
}