Learning to be Safe: Deep RL with a Safety Critic

Krishnan Srinivasan
Krishnan Srinivasan
Cited by: 0|Bibtex|Views6
Other Links: arxiv.org

Abstract:

Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself. A natural first approach toward safe RL is to manually specify constraints on the policy's behavior. However, just as learning has enabled progress in large-scale development ...More

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