Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology

arXiv:2404.04205v1 Announce Type: new
Abstract: The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) …

abstract arxiv challenges cs.ai cs.lg data decision devices environments generated harness intelligence intelligent internet internet of things iot making methodology opportunities presenting reinforcement reinforcement learning struggle transformer type