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dc.contributor.authorJaunet, Theoen_US
dc.contributor.authorVuillemot, Romainen_US
dc.contributor.authorWolf, Christianen_US
dc.contributor.editorViola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatianaen_US
dc.description.abstractWe present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning. This memory is composed of large temporal vectors updated when the agent moves in an environment and is not trivial to understand due to the number of dimensions, dependencies to past vectors, spatial/temporal correlations, and co-correlation between dimensions. It is often referred to as a black box as only inputs (images) and outputs (actions) are intelligible for humans. Using DRLViz, experts are assisted to interpret decisions using memory reduction interactions, and to investigate the role of parts of the memory when errors have been made (e.g. wrong direction). We report on DRLViz applied in the context of video games simulators (ViZDoom) for a navigation scenario with item gathering tasks. We also report on experts evaluation using DRLViz, and applicability of DRLViz to other scenarios and navigation problems beyond simulation games, as well as its contribution to black box models interpretability and explain-ability in the field of visual analytics.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectTheory of computation
dc.subjectReinforcement learning
dc.titleDRLViz: Understanding Decisions and Memory in Deep Reinforcement Learningen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersVisualization Applications and Machine Learning

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  • 39-Issue 3
    EuroVis 2020 - Conference Proceedings

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License