Agentic AI and Autonomous Agents Development Strategies

This survey paper provides a comprehensive overview of the development strategies for agentic AI and autonomous agents, with a focus on Multi-Agent Reinforcement Learning (MARL) techniques. First, it examines the fundamentals of reinforcement learning and deep learning, emphasising their pivotal role in achieving groundbreaking results in diverse domains. Second, the scope of MARL is examined, including its applications in cyber defense, robotics, and autonomous vehicles, as well as its potential to enhance decision-making in dynamic and interactive environments. Third, it also delves into the challenges and research directions in MARL, including trustworthiness, safety, and robustness, and discusses the importance of integrating human factors into autonomous decision-making processes. Finally, it highlights the significance of data-centric frameworks for self-adaptive agents and actor-critic methods in MARL, while also addressing open challenges and future directions, such as scalability, non-stationarity, and explainability. The main objective of this survey paper is to provide a systematic understanding of the current state of agentic AI and autonomous agents development. Most significantly, this work contributes a comprehensive overview of MARL techniques, an examination of the ethical considerations and societal impacts of autonomous agents, and a discussion of the prospects and challenges in the field. We hope this paper can guide researchers, practitioners, and policymakers in the development and deployment of agentic AI and autonomous agents, with a focus on ensuring responsible and beneficial outcomes.