Explaining Reinforcement Learning to Mere Mortals: An Empirical Study

Andrew Anderson; Jonathan Dodge; Amrita Sadarangani; Zoe Juozapaitis; Evan Newman; Jed Irvine; Souti Chattopadhyay; Alan Fern; Margaret Burnett

arXiv · 1903.09708

Explaining Reinforcement Learning to Mere Mortals: An Empirical Study

Andrew Anderson·Jonathan Dodge·Amrita Sadarangani·Zoe Juozapaitis·Evan Newman·Jed Irvine·Souti Chattopadhyay·Alan Fern·Margaret Burnett

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Abstract

We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents. We investigate both a common RL visualization, saliency maps (the focus of attention), and a more recent explanation type, reward-decomposition bars (predictions of future types of rewards). We designed a 124 participant, four-treatment experiment to compare participants' mental models of an RL agent in a simple Real-Time Strategy (RTS) game. Our results show that the combination of both saliency and reward bars were needed to achieve a statistically significant improvement in mental model score over the control. In addition, our qualitative analysis of the data reveals a number of effects for further study.

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Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, Margaret Burnett. 2019-06-18. Explaining Reinforcement Learning to Mere Mortals: An Empirical Study. https://arxiv.org/abs/1903.09708

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