Robust Parameter Learning for Uncertain MDPs
Learning-based approaches to verifying unknown Markov decision processes (MDPs) often employ uncertain MDPs. These models use, for example, confidence intervals to capture transition uncertainty and allow synthesis of policies that are robust to this uncertainty. However, this approach typically quantifies uncertainty independently for individual transition probabilities, ignoring dependencies due to shared latent quantities. We propose to learn such models using parametric MDPs (pMDPs), where transition probabilities are expressions over a set of parameters. We project statistical uncertainty from empirical transition frequencies onto the pMDP's parameter space, yielding a probably appro...
| Authors | Yannik Schnitzer, Alessandro Abate, David Parker |
|---|---|
| Primary category | cs.LG |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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