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arXiv · 2006.03745

Re-understanding Finite-State Representations of Recurrent Policy Networks

Mohamad H. Danesh·Anurag Koul·Alan Fern·Saeed Khorram
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Abstract

We introduce an approach for understanding control policies represented as recurrent neural networks. Recent work has approached this problem by transforming such recurrent policy networks into finite-state machines (FSM) and then analyzing the equivalent minimized FSM. While this led to interesting insights, the minimization process can obscure a deeper understanding of a machine's operation by merging states that are semantically distinct. To address this issue, we introduce an analysis approach that starts with an unminimized FSM and applies more-interpretable reductions that preserve the key decision points of the policy. We also contribute an attention tool to attain a deeper understanding of the role of observations in the decisions. Our case studies on 7 Atari games and 3 control benchmarks demonstrate that the approach can reveal insights that have not been previously noticed.

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Mohamad H. Danesh, Anurag Koul, Alan Fern, Saeed Khorram. 2020-06-06. Re-understanding Finite-State Representations of Recurrent Policy Networks. https://arxiv.org/abs/2006.03745

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