Algorithmic Innovations in Multi-Agent Reinforcement Learning: A Pathway for Smart Cities

Abstract

The concept of smart cities has emerged as an instrumental solution to tackle the intricate challenges associated with urbanization in the twenty-first century. Among the myriad of issues that smart cities aim to address, key concerns such as efficient traffic management, sustainable energy usage, resilient infrastructure, and enhanced public safety are at the forefront. Notably, the implementation of multi-agent reinforcement learning (MARL) has garnered significant attention for its potential role in realizing the vision of smart cities. This chapter serves as an exploration of the frontiers of algorithmic innovation within MARL and its direct applicability to address the complex challenges of urban smart grid systems. The integration of MARL principles is vital in comprehensively modeling the intricate, interdependent urban systems that underpin the smart city framework. Particularly, we emphasize the relevance of MARL in providing adaptive solutions to the intricate dynamics of the urban smart grid, ensuring effective management of energy resources and demand-side management.