3 papers
cs.AI2025
Near-Optimal Reinforcement Learning for Constrained Recurrence Objectives
Dominik Wagner, Leon Witzman, Luke Ong
Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision processes (MDPs) and form the core of $…
cs.LG2024
Reinforcement Learning with LTL and -Regular Objectives via Optimality-Preserving Translation to Average Rewards
Xuan-Bach Le, Dominik Wagner, Leon Witzman +2
Linear temporal logic (LTL) and, more generally, -regular objectives are alternatives to the traditional discount sum and average reward objectives in reinforcement learning (RL…
cs.FL2023
A Coalgebraic Approach to Reducing Finitary Automata
Keri D'Angelo, Alexandra Silva, Gerco van Heerdt +1
Compact representations of automata are important for efficiency. In this paper, we study methods to compute reduced automata, in which no two states accept the same language. We d…