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Interactive Reinforcement Learning for Autonomous Behavior Design

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Artificial Intelligence for Human Computer Interaction: A Modern Approach

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Abstract

Reinforcement Learning (RL) is a machine learning approach based on how humans and animals learn new behaviors by actively exploring their environment that provides them positive and negative rewards. The interactive RL approach incorporates a human-in-the-loop that can guide a learning RL-based agent to personalize its behavior and/or speed up its learning process. To enable HCI researchers to make advances in this area, we introduce an interactive RL framework that outlines HCI challenges in the domain. By following this taxonomy, HCI researchers can (1) design new interaction techniques and (2) propose new applications. To help the role (1) researchers, we describe how different types of human feedback can adapt an RL model to perform as the users intend. We help researchers perform the role (2) by proposing generic design principles to create effective RL applications. Finally, we list current open challenges in interactive RL and what we consider the most promising research directions in this research area.

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Acknowledgements

This work was supported by JST CREST Grant Number JPMJCR17A1, Japan. Additionally, we would like to thank the reviewers of our original DIS paper. Their kind suggestions helped to improve and clarify this manuscript.

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  1. The University of Tokyo, Tokyo, Japan

    Christian Arzate Cruz & Takeo Igarashi

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  1. Christian Arzate Cruz
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  1. Google Research (United States), Mountain View, CA, USA

    Yang Li

  2. Advanced Interactive Technologies Lab, ETH Zurich, Zurich, Switzerland

    Otmar Hilliges

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Arzate Cruz, C., Igarashi, T. (2021). Interactive Reinforcement Learning for Autonomous Behavior Design. In: Li, Y., Hilliges, O. (eds) Artificial Intelligence for Human Computer Interaction: A Modern Approach. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-82681-9_11

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