Reinforcement learning in autonomous defense systems: Strategic applications and challenges
Department of Computer Science, Florida Institute of Technology, USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 140–152.
Article DOI: 10.30574/wjaets.2024.13.1.0383
Publication history:
Received on 23 July 2024; revised on 07 September 2024; accepted on 10 September 2024
Abstract:
Reinforcement learning (RL) is one of the most progressive ways to improve the efficiency of drones and robotic systems in the context of the defense and security industries. In Anomaly Detection (AD) systems, this paper discusses the potential of RL as a Model Selection Criteria (MSC) method and its associated issues. RL algorithms enable such systems to adapt to and learn from interactions with their environment, optimizing their response to complex and dynamic threats. This way, new states may be introduced with better decision-making processes, improving the effectiveness of the operations and enabling Reinforcement learning -powered systems to learn new scenarios as well as perform detailed defensive actions in a real-time context. The importance of RL in national defense can therefore be said to lie in its capacities for changing how threats are identified, how threats are responded to, and even what strategies of defense are thought of. Self-governing systems that incorporate Reinforcement learning are capable of functioning in an unpredictable environment, evaluating threats correctly, and carrying out defensive measures with the help of people practically at all. This versatility is critical in modern warfare because a preferred option is the right response to threats that were unknown a few years ago. However, incorporating RL into autonomous defense systems is not without big challenges. Some important problem areas are improved stability and accuracy of RL algorithms in various and critical situations, the legal and practical consequences of autonomous decision-making and possible threats that can arise due to adversarial manipulation of learning algorithms. In addition, such systems have to be developed and put into practice under national and international standards to meet certain requirements and build confidence in such applications. This paper explores these strategic uses and issues and presents a detailed overview of how RL can improve independent defense functionalities, as well as the significant problems relating to this approach. Thus, by providing case studies and theoretical analysis, it aims to demonstrate that RL has the potential to define the technological development of defense and benefit national security systems in the context of a growing threat.
Keywords:
Robotics; Defense Technology; Reinforcement Learning (RL); Threat Detection; Adversarial Attacks; Defense Technology; Strategic Applications; Adaptive Technology
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0