@inproceedings{
le2024reinforcement,
title={Reinforcement Learning with {LTL} and \${\textbackslash}omega\$-Regular Objectives via Optimality-Preserving Translation to Average Rewards},
author={Xuan-Bach Le and Dominik Wagner and Leon Witzman and Alexander Rabinovich and Luke Ong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=iykao97YXf}
}
Linear temporal logic (LTL) and, more generally, $\omega$-regular objectives are alternatives to the traditional discount sum and average reward objectives in reinforcement learning (RL), offering the advantage of greater comprehensibility and hence explainability. In this work, we study the relationship between these objectives. Our main result is that each RL problem for $\omega$-regular objectives can be reduced to a limit-average reward problem in an optimality-preserving fashion, via (finite-memory) reward machines. Furthermore, we demonstrate the efficacy of this approach by showing that optimal policies for limit-average problems can be found asymptotically by solving a sequence of discount-sum problems approximately. Consequently, we resolve an open problem: optimal policies for LTL and $\omega$-regular objectives can be learned asymptotically.
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards
Xuan-Bach Le Dominik Wagner Leon Witzman Alexander Rabinovich Luke Ong