Reinforcement Learning in Social Sciences: A Survey

Abstract

Reinforcement Learning (RL) has become one of the most prominent topics in artificial intelligence research. It is widely used in various fields, such as recommendation systems, psychology, economics, and natural language dialogue systems. Finding the best path of action to maximize cumulative reward is the long-term strategy of RL. Undertaking research may yield suboptimal immediate results but optimal long-term consequences. Economists can address difficult behavioral problems with knowledge, especially those generated by deep learning algorithms. We provide the most recent advancements in RL methods in this study, along with their applications in gaming, finance, and economics. The survey's last section discusses RL's present problems and potential future developments. Such open problems as sample efficiency, safety, and interpretability are currently being sought after by researchers. Moreover, several ambitious prospective applications of RL in a wide variety of domains are discussed. This study gives a comprehensive review of the many methods and uses of RL in social science. This study's results will give researchers a standard against which to evaluate the utility and efficacy of frequently used RL. Guide future investigations across several domains.