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Explaining Reinforcement Learning to Mere Mortals: An Empirical Study
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.Comment: 7 page