<ul><li>A learning-based approach is proposed to synthesize policies for huge parameterized Markov decision processes (MDPs).</li><li>The method generalizes optimal policies obtained from model-checking small instances to larger ones using decision-tree learning.</li><li>By bypassing the need for explicit state-space exploration of large models, the method provides a practical solution to the state-space explosion problem.</li><li>Experimental results show that the policies perform well even for models beyond the reach of state-of-the-art analysis tools.</li></ul>

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

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