A survey of collaborative reinforcement learning: interactive methods and design patterns

Li, Zhaoxing, Shi, Lei, Cristea, Alexandra I. and Zhou, Yunzhan (2021) A survey of collaborative reinforcement learning: interactive methods and design patterns. Ju, Wendy, Oehlberg, Lora, Follmer, Sean, Fox, Sarah and Kuznetsov, Stacey (eds.) In DIS '21: Proceedings of the 2021 ACM Designing Interactive Systems Conference. Association for Computing Machinery. pp. 1579-1590 . (doi:10.1145/3461778.3462135).

Abstract

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.

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Contributors

Author: Zhaoxing Li

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