Skip to yearly menu bar Skip to main content


Re-understanding Finite-State Representations of Recurrent Policy Networks

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

[ ] [ Livestream: Visit Reinforcement Learning 3 ] [ Paper ]
[ Paper ]


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.

Chat is not available.