Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2021

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.

Discipline

OS and Networks

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

NIPS'21: Proceedings of the 35th International Conference on Neural Information Processing Systems, Virtual Conference, December 6-14

Volume

34

First Page

10171

Last Page

10185

ISBN

9781713845393

Identifier

10.5555/3540261.3541039

Publisher

Curran Associates, Inc.

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.5555/3540261.3541039

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