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Citeable URL: https://ir.library.oregonstate.edu/concern/defaults/8k71np40p

Descriptions

Attribute Name	Values
Creator	Anderson, Andrew A.
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.
License	Attribution-ShareAlike 4.0 (CC BY-SA 4.0)
Resource Type	Other
Date Available	2019-08-10
Academic Affiliation	College of Engineering
Subject	Intelligent agents (Computer software) Human-computer interaction Reinforcement learning
Rights Statement	No Copyright - Non-Commercial Use Only
Publisher	Oregon State University
Language	English [eng]
Accessibility Feature	unknown

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