Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning
History-dependent policies induced by recurrent neural networks (RNNs) rely on latent hidden state dynamics, making verification in partially observable reinforcement learning (RL) challenging. Existing RNN verification tools typically rely on restrictive modeling assumptions or coarse over-approximations of the hidden state space, which can lead to overly conservative or inconclusive results. We propose $\textbf{RNN}$ $\textbf{Pro}$babilistic $\textbf{Ve}$rification ($\texttt{RNN-ProVe}$), a probabilistic framework that $\textit{estimates the likelihood}$ of undesired behaviors in RNN-based policies. $\texttt{RNN-ProVe}$ uses policy-driven sampling to approximate the set of hidden states...
| Authors | Luca Marzari, Enrico Marchesini |
|---|---|
| Primary category | cs.AI |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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