Harnessing Online Knowledge Transfer for Enhanced Search and Rescue Decisions via Multi-Agent Reinforcement Learning

Abstract

In the rapidly evolving domain of the Internet of Things (IoT), devices play an instrumental role in high-stakes scenarios like search and rescue (SAR) operations. Traditional decision-making processes within SAR missions often struggle to cope with the dynamic and unpredictable nature of such environments, leading to inefficiencies and delayed responses. This paper aims to explore the potential of multi-agent reinforcement learning (MARL) to improve the decision-making process within SAR operations underpinned by IoT. Functional, current methods are limited by their static decision frameworks and inability to adapt in real time to the chaotic variables present in SAR situations. We introduced a novel MARL framework and compared its performance against benchmark strategies, specifically the multi-agent deep deterministic policy gradient (MADDPG) approach. Uniquely enhanced by online knowledge transfer, the framework leverages the capabilities of the deep deterministic policy gradient (DDPG) method. The preliminary findings underscore the proposed framework’s superior efficiency and speed in SAR contexts. Our research highlights MARL’s transformative potential, positing it as a groundbreaking strategy for IoT-based decision making in high-pressure SAR environments with suggestions for further studies in varied real-world scenarios.