Reinforcement Learning of Emerging Swarm Technologies: A Literature Review

In this paper, we conduct a comprehensive review of the integration of reinforcement learning (RL) and swarm intelligence (SI) techniques to address the challenges of optimization and decision-making in autonomous systems operating in complex environments. Drawing upon previous research and case studies in various domains, including robot swarms, power grids, and unmanned aerial vehicles (UAVs), we analyze the effectiveness of synergistic optimization approaches. Our review highlights the potential of combining RL and SI algorithms to achieve adaptive and robust decision-making mechanisms, capable of addressing dynamic conditions and uncertainties. Swarm Intelligence (SI) refers to the collective behavior of decentralized, self-organized systems, typically inspired by the behavior of social insects, such as ants, bees, and termites, as well as other animal societies. In the context of the document, SI techniques are algorithms that imitate the behavior of these collectives to address complex optimization and decision-making challenges in autonomous systems. These algorithms are designed to enable adaptive and robust decision-making mechanisms capable of addressing dynamic conditions and uncertainties. The document highlights the potential of combining SI algorithms with reinforcement learning (RL) to enhance the performance, scalability, and robustness of autonomous systems across diverse application domains. Additionally, the document discusses various SI algorithms, their sources of inspiration, applications, and future research directions, emphasizing their effectiveness in solving complex optimization tasks across different areas. Through a detailed examination of existing literature and empirical evaluations, we provide insights into the strengths, limitations, and future directions of this interdisciplinary research area. Additionally, we present an analysis of the distribution of research papers across different fields, shedding light on the number of studies conducted in each domain. By synthesizing key findings from previous studies, our work contributes to the understanding of how RL and SI integration can enhance the performance, scalability, and robustness of autonomous systems across diverse application domains.