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A Framework for Understanding and Visualizing Strategies of RL Agents. (arXiv:2208.08552v1 [cs.AI])
Aug. 19, 2022, 1:10 a.m. | Pedro Sequeira, Daniel Elenius, Jesse Hostetler, Melinda Gervasio
cs.LG updates on arXiv.org arxiv.org
Recent years have seen significant advances in explainable AI as the need to
understand deep learning models has gained importance with the increased
emphasis on trust and ethics in AI. Comprehensible models for sequential
decision tasks are a particular challenge as they require understanding not
only individual predictions but a series of predictions that interact with
environmental dynamics. We present a framework for learning comprehensible
models of sequential decision tasks in which agent strategies are characterized
using temporal logic formulas. …
More from arxiv.org / cs.LG updates on arXiv.org
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