1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization

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AZEEM Muqsit CHAKRABORTY Debraj KANAV Sudeep KŘETÍNSKÝ Jan MOHAGHEGHI Mohammadsadegh MOHR Stefanie WEININGER Maximilian

Rok publikování 2025
Druh Článek ve sborníku
Konference VMCAI 2025, 26th International Conference on Verification, Model Checking, and Abstract Interpretation
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
Doi http://dx.doi.org/10.1007/978-3-031-82703-7_5
Klíčová slova model checking; probabilistic verification; Markov decision process; policy synthesis
Popis Despite the advances in probabilistic model checking, the scalability of the verification methods remains limited. In particular, the state space often becomes extremely large when instantiating parameterized Markov decision processes (MDPs) even with moderate values. Synthesizing policies for such huge MDPs is beyond the reach of available tools. We propose a learning-based approach to obtain a reasonable policy for such huge MDPs. The idea is to generalize optimal policies obtained by model-checking small instances to larger ones using decision-tree learning. Consequently, our method bypasses the need for explicit state-space exploration of large models, providing a practical solution to the state-space explosion problem. We demonstrate the efficacy of our approach by performing extensive experimentation on the relevant models from the quantitative verification benchmark set. The experimental results indicate that our policies perform well, even when the size of the model is orders of magnitude beyond the reach of state-of-the-art analysis tools.
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