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Creators/Authors contains: "Greg Anderson"

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  1. (, ICLR 2023)
    Accepted Manuscript Full Text Available
  2. ; ; (, International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  3. ; ; (, International Conference on Learning Representations)
    In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training. We present a novel neurosymbolic approach called SPICE to solve this safe exploration problem. SPICE uses an online shielding layer based on symbolic weakest preconditions to achieve a more precise safety analysis than existing tools without unduly impacting the training process. We evaluate the approach on a suite of continuous control benchmarks and show that it can achieve comparable performance to existing safe learning techniques while incurring fewer safety violations. Additionally, we present theoretical results showing that SPICE converges to the optimal safe policy under reasonable assumptions. 
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    Accepted Manuscript Full Text Available
  4. (, Proceedings of the Conference on Neural Information Processing)
    null (Ed.)
    Accepted Manuscript Full Text Available
  5. (, International Conference on Computer Aided Verification)
    Full Text Available