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arXiv · 2207.06269

Policy Optimization with Sparse Global Contrastive Explanations

Jiayu Yao·Sonali Parbhoo·Weiwei Pan·Finale Doshi-Velez
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Abstract

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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Jiayu Yao, Sonali Parbhoo, Weiwei Pan, Finale Doshi-Velez. 2022-07-13. Policy Optimization with Sparse Global Contrastive Explanations. https://arxiv.org/abs/2207.06269

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