Transforming IoT Decision-Making: A Transformer-Enhanced Reinforcement Learning Approach

This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to enhance decision-making capabilities in complex Internet of Things (IoT) environments. By leveraging the self-attention mechanism of transformers, the proposed approach improves reinforcement learning (RL) agents' ability to understand and navigate dynamic IoT ecosystems, leading to significant advancements in decision-making efficiency and adaptability.