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arXiv · 1808.09572

Cycle-of-Learning for Autonomous Systems from Human Interaction

Nicholas R. Waytowich·Vinicius G. Goecks·Vernon J. Lawhern
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Abstract

We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms. We provide a taxonomy to categorize the types of human interaction and present our Cycle-of-Learning framework for autonomous systems that combines different human-interaction modalities with reinforcement learning. Two key concepts provided by our Cycle-of-Learning framework are how it handles the integration of the different human-interaction modalities (demonstration, intervention, and evaluation) and how to define the switching criteria between them.

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Nicholas R. Waytowich, Vinicius G. Goecks, Vernon J. Lawhern. 2018-08-28. Cycle-of-Learning for Autonomous Systems from Human Interaction. https://arxiv.org/abs/1808.09572

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