Presents hierarchical adaptive refinement to accelerate near-optimal policy synthesis in MDPs up to 1M states with up to 2x speedup over PRISM and formal error bounds.
Learning-based probabilistic ltl motion planning with environment and motion uncertainties,
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Accelerating Policy Synthesis in Large-Scale MDPs via Hierarchical Adaptive Refinement
Presents hierarchical adaptive refinement to accelerate near-optimal policy synthesis in MDPs up to 1M states with up to 2x speedup over PRISM and formal error bounds.