title: Infinite Time Horizon Safety of Bayesian Neural Networks
abstract: Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction.
  We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems.
  Compared to the existing sampling-based approaches, which are inapplicable to the infinite time horizon setting, we train a separate deterministic neural network that serves as an infinite time horizon safety certificate.
  In particular, we show that the certificate network guarantees the safety of the system over a subset of the BNN weight posterior's support. Our method first computes a safe weight set and then alters the BNN's weight posterior to reject samples outside this set. Moreover, we show how to extend our approach to a safe-exploration reinforcement learning setting, in order to avoid unsafe trajectories during the training of the policy. 
  We evaluate our approach on a series of reinforcement learning benchmarks, including non-Lyapunovian safety specifications.
authors: Mathias Lechner Đorđe Žikelić Krishnendu Chatterjee Thomas A Henzinger
hIndex: Thomas A Henzinger 100