Explaining Reinforcement Learning to Mere Mortals: An Empirical Study [article]

Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, Margaret Burnett
<span title="2019-06-18">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... t 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.09708v2">arXiv:1903.09708v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e7b3wft65jb2rlbqoli2z2uc44">fatcat:e7b3wft65jb2rlbqoli2z2uc44</a> </span>
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