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Probabilistic Verification of Recurrent Neural Netw... | AI Research

Key Takeaways

  • Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning introduces a new framework called RNN-ProVe designe...
  • 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.
  • While RNNs are excellent at handling history-dependent tasks—such as navigating environments where only partial information is available—they are notoriously difficult to verify.
Paper AbstractExpand

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.

Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learning introduces a new framework called RNN-ProVe designed to assess the safety and reliability of recurrent neural networks (RNNs) used in reinforcement learning. While RNNs are excellent at handling history-dependent tasks—such as navigating environments where only partial information is available—they are notoriously difficult to verify. Existing tools often rely on overly broad approximations that treat all possible hidden states as equally likely, leading to "false alarms" or inconclusive results. RNN-ProVe shifts the focus to estimating the likelihood of undesired behaviors specifically within the set of hidden states that are actually reachable by a trained policy.

The Challenge of Hidden States

In reinforcement learning, an RNN uses a "hidden state" to remember past observations and actions. To verify if a policy is safe, one must check if any of these hidden states could lead to an unsafe action. Traditional verification methods often use geometric shapes (like boxes) to cover the entire space of possible hidden states. However, because these shapes are often too large, they include "infeasible" histories—scenarios that the agent would never actually encounter. This leads to conservative, inaccurate, and often unhelpful safety certificates.

How RNN-ProVe Works

RNN-ProVe addresses this by using a probabilistic approach rather than trying to map every possible state exactly. The framework operates in two main steps: 1. Feasibility Learning: It trains a classifier to act as a "feasibility oracle." By observing the agent during training, the classifier learns to distinguish between hidden states that are realistically reachable and those that are not. 2. Policy-Driven Sampling: Instead of sampling the entire space, the framework uses this oracle to focus on "probabilistically feasible" histories. It then applies Monte Carlo estimation to calculate the likelihood of undesired actions, providing statistical error bounds that ensure the results are high-confidence and reliable.

Scaling to Multi-Agent Systems

A significant contribution of this work is its extension to cooperative multi-agent reinforcement learning (MARL). In these settings, multiple agents must coordinate to achieve a shared goal. RNN-ProVe treats the verification of the entire system as a collection of independent tasks for each agent. By identifying the "worst-performing" agent—the one with the highest probability of triggering an undesired behavior—the framework provides a clear, quantitative safety assessment for the entire multi-agent team without requiring complex, centralized computations.

Why This Matters

The researchers demonstrate that RNN-ProVe is more precise than existing tools, offering quantitative guarantees that are better aligned with how RL agents actually behave. By moving away from the computationally impossible task of exact verification (which is #P-hard) and toward a principled, probabilistic estimation, the framework provides a practical way to ensure safety in complex, history-dependent decision-making systems. This approach allows developers to move beyond simple "safe/unsafe" labels and instead understand the specific level of risk associated with their AI policies.

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