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) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents' capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decision-making efficiency and adaptability. Our contributions include a detailed exploration of the transformer's role in processing heterogeneous IoT data, a comprehensive evaluation of the framework's performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape.

翻译：物联网（IoT）的快速发展导致互联设备生成的数据量激增，为复杂环境中的智能决策带来了机遇与挑战。传统强化学习（RL）方法因处理与解释物联网应用中固有复杂模式与依赖关系的能力有限，往往难以充分利用这些数据。本文提出一种新型框架，将Transformer架构与近端策略优化（PPO）相结合以应对上述挑战。通过利用Transformer的自注意力机制，我们的方法增强了强化学习代理在动态物联网环境中理解与行动的能力，从而优化决策过程。我们在多种物联网场景中验证了该方法的有效性——从智能家居自动化到工业控制系统——其在决策效率与适应性方面均表现出显著提升。本文的贡献包括：深入探讨Transformer在处理异构物联网数据中的角色，全面评估该框架在不同环境中的性能，以及与传统强化学习方法的基准对比。结果表明，该方法在使强化学习代理应对物联网生态系统的复杂性方面取得了显著进展，凸显了其革新物联网智能自动化与决策的潜力。