Reinforcement learning and the Metaverse: a symbiotic collaboration

Abstract

The Metaverse is an emerging virtual reality space that merges digital and physical worlds and provides users with immersive, interactive, and persistent virtual environments. The Metaverse leverages multiple technologies, including digital twins, blockchain, artificial intelligence, extended reality, and edge computing to realize the seamless connectivity and interaction between both worlds: physical and virtual. Artificial Intelligence (AI) empowers intelligent decisions in such complex dynamic environments. More specifically, Reinforcement Learning (RL) is uniquely effective in the context of Metaverse applications due to the natural process of learning through interaction and its modeling of sequential decision making, allowing it to be flexible, dynamic, and able to discover complex strategies and emergent behavior in complicated environments where programming explicit rules is impractical. Although multiple works have explored the research on the Metaverse and AI-based applications, there remains a significant gap in the literature that addresses the contribution of RL algorithms within the Metaverse. Therefore, this review presents a comprehensive overview of RL algorithms for Metaverse applications. We examine the architecture of Metaverse networks, the role of RL in enhancing virtual interactions, and the potential for transferring learned behaviors to real-world applications. Furthermore, we categorize the key challenges, opportunities, and research directions associated with deploying RL in the Metaverse.