Abstract

This book chapter explores the integration of Neural Architecture Search (NAS) and Reinforcement Learning (RL) to optimize the performance of autonomous systems. By combining the adaptive capabilities of NAS with the decision-making prowess of RL, this approach aims to create more efficient, scalable, and intelligent systems capable of tackling complex tasks. The chapter delves into the synergies between NAS and RL, highlighting their potential to improve system robustness, adaptability, and generalization across dynamic environments. It addresses the challenges involved in integrating these technologies, such as computational complexity, overfitting, and the balance between exploration and exploitation. Through comparative analyses with traditional methods, the chapter underscores the advantages of NAS-RL systems in benchmark tasks, showcasing their superiority in task-specific optimization and dynamic task adaptation. This comprehensive discussion provides valuable insights for researchers and practitioners aiming to advance autonomous system design through cutting-edge AI techniques.

Introduction

Autonomous systems have become integral to a wide range of applications, including robotics, self-driving cars, drones, and intelligent decision-making systems [1,2]. These systems are designed to operate independently, adapting to their environment and executing complex tasks without human intervention [3-5]. The rapid advancement in artificial intelligence (AI) and machine learning (ML) has enabled the development of such systems, yet the challenges of optimizing their performance in dynamic and unpredictable environments persist [6,7]. As autonomous systems tackle increasingly complex real-world tasks, ensuring their efficiency, robustness, and scalability was critical [8]. One promising approach to achieving these objectives was by combining Neural Architecture Search (NAS) with Reinforcement Learning (RL) [9]. This combination aims to enhance the decision-making and adaptability of autonomous systems while optimizing their underlying neural architectures [10,11].

Neural Architecture Search (NAS) has emerged as a transformative technique that automates the design of neural networks [12]. Traditionally, neural network architectures were manually designed, relying on the expertise of researchers and practitioners [13]. However, this manual process can be time-consuming and limited in its ability to explore diverse design spaces [14]. NAS automates the discovery of optimal architectures by leveraging search algorithms, allowing for the exploration of a wide range of neural network configurations [15,16]. By applying NAS, researchers can identify architectures that are well-suited to specific tasks, leading to improved performance and reduced resource consumption [17,18]. NAS allows for the development of highly specialized neural architectures that can adapt to the unique demands of different environments, thus improving the efficiency and effectiveness of these systems [19,20].