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 Web site:||https://doi.org/10.1145/3461778.3462135|
|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 https://doi.org/10.1145/3461778.3462135|
|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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