2026· International Conference on Concurrency Theory· pp. 4:1-4:17· 0 citations· 17 references
Computer Science
TL;DR
The main results obtained for both semantics since the introduction of the model in 2014 are surveyed, which cover reachability, parity and Rabin objectives, under the qualitative criteria (almost-sure, limit-sure and the quantitative value-threshold problem, and the key algorithmic ideas are outlined.
Interval Markov decision processes (IMDPs) provide a natural framework for modeling stochastic systems with uncertain transition probabilities, represented by probability intervals and resolved adversarially. Such uncertainty arises naturally, for example, when the transition model is learned from finite data or obtain...
Sarvin Bahmani, Soumyajit Paul, Sven Schewe et al.· 0 citations
Robust Markov Decision Processes (RMDPs) generalize classical MDPs by allowing uncertainty in transition probabilities and optimizing against their worst-case realization. We consider $(s,a)$-rectangular RMDPs with \emph{linearly defined} uncertainty sets and study parity objectives, which are a canonical representatio...
Ali Asadi, Krishnendu Chatterjee, E. Goharshady et al.· 0 citations
Well-structured transition systems (WSTS) provide a classical framework for the verification of infinite-state systems, but their probabilistic extensions lack a unified treatment of quantitative coverability: path-enumeration algorithms assume a finite branching degree, while alternative approximation schemes defer so...
Raphaëlle Faure, Alain Finkel, Gaspard Fougea et al.· 0 citations
Robust POMDPs (RPOMDPs) generalize classical POMDPs to the setting where exact transition probabilities are not known -- rather, they are only known to belong to some uncertainty set of values. In this work, we study the problem of solving RPOMDPs with general omega-regular objectives, which subsume a broad class of ob...
D. Latha, Dion Reji, S. Akshay et al.· 0 citations
Non-Markovian environments are often modeled as Regular Decision Processes (RDPs), where dynamics depend on the interaction history through a finite automaton. Existing offline guarantees for RDPs rely on a distinguishability assumption on the behaviour policy but provide no means of verifying it. When the assumption i...
Reinforcement learning (RL) for reachability specifications is fundamental to sequential decision-making. Prior work establishes asymptotic convergence to optimal policies, but only through model-based methods that must explicitly estimate the transition probabilities of the underlying Markov Decision Process (MDP). We...
Lu-Chin Chang, Suguman Bansal· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
