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Policy Optimization with Sparse Global Contrastive Explanations

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arxiv 2207.06269 v1 pith:OHUJH2YV submitted 2022-07-13 cs.LG

Jiayu Yao , Sonali Parbhoo , Weiwei Pan , Finale Doshi-Velez This is my paper · ORCID
classification cs.LG
keywords policycontrastiveglobalsparsechangesexplanationframeworkminimal
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We develop a Reinforcement Learning (RL) framework for improving an existing behavior policy via sparse, user-interpretable changes. Our goal is to make minimal changes while gaining as much benefit as possible. We define a minimal change as having a sparse, global contrastive explanation between the original and proposed policy. We improve the current policy with the constraint of keeping that global contrastive explanation short. We demonstrate our framework with a discrete MDP and a continuous 2D navigation domain.

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    Strategic link scores quantify dependencies between planned actions as the drop in probability of a set-up action when its pay-off action is constrained, with applications to RL explanation, safe recommendations, and ...

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