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.
@article{arxiv.1903.09708,
title = {Explaining Reinforcement Learning to Mere Mortals: An Empirical Study},
author = {Andrew Anderson and Jonathan Dodge and Amrita Sadarangani and Zoe Juozapaitis and Evan Newman and Jed Irvine and Souti Chattopadhyay and Alan Fern and Margaret Burnett},
journal= {arXiv preprint arXiv:1903.09708},
year = {2019}
}