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

Zhaoxing Li, Lei Shi, Alexandra I. Cristea, Yunzhan Zhou
2021 Designing Interactive Systems Conference 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
more » ... istic 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.
doi:10.1145/3461778.3462135 fatcat:5f5ydpvp6fgkzmggxgs32sjdty