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

Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav, Jan Kretínský, MohammadSadegh Mohagheghi, Stefanie Mohr, Maximilian Weininger

Abstract: 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.