Rademics Research Institute

Abstract

This book chapter explores the integration of multi-objective reinforcement learning (MORL) for balancing efficiency and safety in autonomous decision-making. As autonomous systems are increasingly deployed in complex, real-world environments, ensuring both high-performance optimization and safety becomes paramount. The chapter delves into the fundamental concepts of MORL, emphasizing how multiple conflicting objectives can be addressed simultaneously, while maintaining robust safety standards. Various methods, such as risk-sensitive learning, safety constraints, and reward shaping, are examined to effectively guide autonomous agents in navigating trade-offs between exploration and safety. Additionally, the chapter discusses policy-based methods, verification, and validation techniques crucial for real-world implementation. With a focus on real-world testing for safety assurance, it presents strategies for integrating safety-critical components in decision-making processes. This work provides valuable insights into enhancing the reliability, performance, and safety of autonomous systems in dynamic environments.

Introduction

Autonomous systems have become central to a variety of industries, from self-driving cars and drones to robotics and industrial automation [1,2]. The primary challenge in deploying these systems lies in balancing two critical objectives: optimizing for performance (efficiency) and ensuring safety [3,4]. Multi-objective reinforcement learning (MORL) offers a promising framework to address this challenge, as it can simultaneously optimize multiple objectives with potentially conflicting goals [5,6]. In the context of autonomous decision-making, safety often cannot be sacrificed for efficiency, making it essential to incorporate safety constraints into learning algorithms [7,8]. As these systems operate in dynamic and unpredictable environments, ensuring their robustness while meeting operational goals was essential for their widespread adoption [9,10].

One of the key advantages of MORL lies in its ability to model and balance multiple objectives, such as speed, cost, and safety, which are often at odds in real-world applications [11,12]. In traditional reinforcement learning (RL), a single objective was typically optimized, whichlead to risky behavior if safety was not explicitly integrated [13]. MORL, on the other hand, allows agents to consider multiple factors during decision-making, adjusting their strategies based on the relative importance of each objective [14,15]. This was particularly relevant for autonomous systems, where a failure to consider safety could result in catastrophic outcomes [16]. By exploring MORL techniques, this chapter aims to provide insights into the methods that can balance performance and safety in real-world autonomous systems [17].