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

Li, Zhaoxing; Shi, Lei; Cristea, Alexandra I.; Zhou, Yunzhan

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Zhaoxing Li
PGR Student Doctor of Philosophy

Lei Shi

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Yunzhan Zhou
PGR Student Doctor of Philosophy


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.

Presentation Conference Type Conference Paper (Published)
Conference Name ACM Designing Interactive Systems (DIS)
Start Date Jun 28, 2023
End Date Jul 2, 2021
Acceptance Date Apr 9, 2021
Online Publication Date Jun 28, 2021
Publication Date 2021
Deposit Date Jun 30, 2021
Publicly Available Date Jun 30, 2021
Publisher Association for Computing Machinery (ACM)
Pages 1579-1590
ISBN 9781450384766
Public URL


Accepted Conference Proceeding (640 Kb)

Copyright 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

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