Reconstructing Actions To Explain Deep Reinforcement Learning

no code implementations • 17 Sep 2020 • Xuan Chen, Zifan Wang, Yucai Fan, Bonan Jin, Piotr Mardziel, Carlee Joe-Wong, Anupam Datta

Feature attribution has been a foundational building block for explaining the input feature importance in supervised learning with Deep Neural Network (DNNs), but face new challenges when applied to deep Reinforcement Learning (RL). We propose a new approach to explaining deep RL actions by defining a class of \emph{action reconstruction} functions that mimic the behavior of a network in deep RL.

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