Sound Value Iteration for Simple Stochastic Games
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| Název: | Sound Value Iteration for Simple Stochastic Games |
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| Autoři: | Azeem, Muqsit, Kretinsky, Jan, Weininger, Maximilian |
| Zdroj: | Electronic Proceedings in Theoretical Computer Science. 428:29-44 |
| Publication Status: | Preprint |
| Informace o vydavateli: | Open Publishing Association, 2025. |
| Rok vydání: | 2025 |
| Témata: | Computer Science and Game Theory, FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Computer Science - Computer Science and Game Theory, FOS: Electrical engineering, electronic engineering, information engineering, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Multiagent Systems, Logic in Computer Science (cs.LO), Computer Science and Game Theory (cs.GT), Multiagent Systems (cs.MA) |
| Popis: | Algorithmic analysis of Markov decision processes (MDP) and stochastic games (SG) in practice relies on value-iteration (VI) algorithms. Since basic VI does not provide guarantees on the precision of the result, variants of VI have been proposed that offer such guarantees. In particular, sound value iteration (SVI) not only provides precise lower and upper bounds on the result, but also converges faster in the presence of probabilistic cycles. Unfortunately, it is neither applicable to SG, nor to MDP with end components. In this paper, we extend SVI and cover both cases. The technical challenge consists mainly in proper treatment of end components, which require different handling than in the literature. Moreover, we provide several optimizations of SVI. Finally, we evaluate our prototype implementation experimentally to demonstrate its potential on systems with probabilistic cycles. In Proceedings GandALF 2025, arXiv:2509.13258. A full version of this paper appears at arXiv:2411.11549 |
| Druh dokumentu: | Article |
| Jazyk: | English |
| ISSN: | 2075-2180 |
| DOI: | 10.4204/eptcs.428.4 |
| DOI: | 10.48550/arxiv.2509.14112 |
| DOI: | 10.48550/arxiv.2411.11549 |
| Přístupová URL adresa: | http://arxiv.org/abs/2509.14112 http://arxiv.org/abs/2411.11549 |
| Rights: | CC BY arXiv Non-Exclusive Distribution |
| Přístupové číslo: | edsair.doi.dedup.....c8f32e855ec751ea882dd6c135a21e94 |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Algorithmic analysis of Markov decision processes (MDP) and stochastic games (SG) in practice relies on value-iteration (VI) algorithms. Since basic VI does not provide guarantees on the precision of the result, variants of VI have been proposed that offer such guarantees. In particular, sound value iteration (SVI) not only provides precise lower and upper bounds on the result, but also converges faster in the presence of probabilistic cycles. Unfortunately, it is neither applicable to SG, nor to MDP with end components. In this paper, we extend SVI and cover both cases. The technical challenge consists mainly in proper treatment of end components, which require different handling than in the literature. Moreover, we provide several optimizations of SVI. Finally, we evaluate our prototype implementation experimentally to demonstrate its potential on systems with probabilistic cycles.<br />In Proceedings GandALF 2025, arXiv:2509.13258. A full version of this paper appears at arXiv:2411.11549 |
|---|---|
| ISSN: | 20752180 |
| DOI: | 10.4204/eptcs.428.4 |