In-Person Poster presentation / top 25% paper

Towards Interpretable Deep Reinforcement Learning with Human-Friendly Prototypes

Eoin Kenny · Mycal Tucker · Julie Shah

MH1-2-3-4 #158

Keywords: [ Social Aspects of Machine Learning ] [ Prototypes ] [ deep reinforcement learning ] [ User Study ] [ Interpretable Machine Learning ]


Despite recent success of deep learning models in research settings, their application in sensitive domains remains limited because of their opaque decision-making processes. Taking to this challenge, people have proposed various eXplainable AI (XAI) techniques designed to calibrate trust and understandability of black-box models, with the vast majority of work focused on supervised learning. Here, we focus on making an "interpretable-by-design" deep reinforcement learning agent which is forced to use human-friendly prototypes in its decisions, thus making its reasoning process clear. Our proposed method, dubbed Prototype-Wrapper Network (PW-Net), wraps around any neural agent backbone, and results indicate that it does not worsen performance relative to black-box models. Most importantly, we found in a user study that PW-Nets supported better trust calibration and task performance relative to standard interpretability approaches and black-boxes.

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