AI RESEARCH
Accelerating Policy Synthesis in Large-Scale MDPs via Hierarchical Adaptive Refinement
arXiv CS.AI
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ArXi:2506.17792v2 Announce Type: replace Software-intensive systems, such as software product lines and robotics, utilise Marko decision processes (MDPs) to capture uncertainty and analyse sequential decision-making problems. Despite the usefulness of conventional policy synthesis methods, they fail to scale to large state spaces. Our approach addresses this issue and accelerates policy synthesis in large MDPs by dynamically refining the MDP and iteratively selecting the most fragile MDP regions for refinement.