Verifying reinforcement learning up to infinity
Formally verifying that reinforcement learning systems act safely is increasingly important, but existing methods only verify over finite time. This is of limited use for dynamical systems that run indefinitely. We introduce the first method for verifying the time-unbounded safety of neural networks...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
International Joint Conferences on Artificial Intelligence Organization
2021
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