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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 4·5 min read
PAC Approximation and DIRECT Optimization for Parametric Markov Models
TL;DR
A new approach for optimizing parametric Markov decision processes (pMDPs) combines PAC approximation with the DIRECT optimization algorithm. This method provides efficient parameter synthesis while ensuring bounded error margins in the optimization results.
✦ Why It Matters
Implement this PAC and DIRECT optimization approach to improve your parameter synthesis tasks in pMDPs today.
Key Takeaways
- Utilize PAC approximation for efficient parameter synthesis in pMDPs.
- Apply the DIRECT algorithm for derivative-free global optimization.
- Leverage empirical results to choose between optimization methods.
- Ensure bounded error margins in your optimization results.
How It Works
The scenario approach samples parameter configurations and solves a linear program to create a polynomial approximation of the satisfaction function. This approximation guarantees an error margin for most of the parameter domain, allowing for efficient analysis and optimization.