Other Scholarly Content

 

Supplementary Materials Public Deposited

Downloadable Content

Download file
https://ir.library.oregonstate.edu/concern/defaults/8k71np40p

Descriptions

Attribute NameValues
Creator
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
Resource Type
Date Available
Academic Affiliation
Subject
Rights Statement
Publisher
Language

Relationships

Parents:

This work has no parents.

Items