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A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns

Li, Zhaoxing and Shi, Lei and Cristea, Alexandra I. and Zhou, Yunzhan (2021) 'A Survey of Collaborative Reinforcement Learning: Interactive Methods and Design Patterns.', ACM Designing Interactive Systems (DIS) Virtual, 28 Jun - 02 Jul 2021.


Recently, methods enabling humans and Artificial Intelligent (AI) agents to collaborate towards improving the efficiency of Reinforcement Learning - also called Collaborative Reinforcement Learning (CRL) - have been receiving increasing attention. In this paper, we provide a long-term, in-depth survey, investigating human-AI collaborative methods based on both interactive reinforcement learning algorithms and human-AI collaborative frameworks, between 2011 and 2020. We elucidate and discuss synergistic analysis methods of both the growth of the field and the state-of-the-art; we suggest novel technical directions and new collaboration design ideas. Specifically, we provide a new CRL classification taxonomy, as a systematic modelling tool for selecting and improving new CRL designs. Furthermore, we propose generic CRL challenges providing the research community with a guide towards effective implementation of human-AI collaboration. The aim is to empower researchers to develop more efficient and natural human-AI collaborative methods that could utilise the different strengths of humans and AI.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Publisher statement:© ACM 2021. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in
Date accepted:09 April 2021
Date deposited:30 June 2021
Date of first online publication:28 June 2021
Date first made open access:30 June 2021

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