Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2605.14758v1
• Date: Thu, 14 May 2026 12:22:54 GMT
• Title: Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning
• Abstract summary: Existing verification tools rely on restrictive modeling assumptions or coarse over-approximations of hidden state space.<n>We propose $textbfRNN$ $textbfPro$babilistic $textbfVe$rification ($texttRNN-ProVe$), a probabilistic framework that $textitestimates the likelihood$ of undesired behaviors in RNN-based policies.<n>Experiments on partially observable single-agent and cooperative multi-agent tasks show that $texttRNN-ProVe$ yields more quantitative,
• Score: 7.340828059560292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 that are feasible under a trained policy, and derives statistical error bounds to produce bounded-error, high-confidence estimates of behavioral violations. Experiments on partially observable single-agent and cooperative multi-agent tasks show that $\texttt{RNN-ProVe}$ yields more quantitative, feasibility-aware probabilistic guarantees than existing tools, while scaling to recurrent and multi-agent settings.

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