In the era of exponential growth in the Internet of Things, the surge in connected devices has given rise to a multitude of security challenges, encompassing attack vectors, vulnerabilities, and security holes. This survey meticulously explores the landscape of IoT attacks and security concerns, offering an in-depth examination of cyber threats targeting various IoT systems. Emphasizing the critical imperative to secure billions of interconnected devices, the survey underscores the necessity to fully unlock the potential of IoT applications. Amid these security challenges, researchers are increasingly turning to machine learning, with a particular focus on reinforcement learning, as a promising avenue to fortify IoT systems. The distinctive ability of reinforcement learning to autonomously adapt to the environment with minimal input, dynamically adjust settings, and solve optimization problems positions it as a compelling solution to address the dynamic and evolving nature of IoT security threats. It categorizes and analyzes a spectrum of cyberattacks, mapping them to different tiers of the fundamental IoT architecture. Additionally, it provides a systematic overview of security measures grounded in reinforcement learning, offering insights into how these solutions can effectively counter diverse cyber threats against IoT systems. Visual aids, including tables summarizing key current attacks and defenses in the IoT security landscape utilizing reinforcement learning, enhance the accessibility of the survey. This study intends to contribute to a deeper understanding of the complex interactions between cybersecurity and the rapidly growing IoT ecosystem by offering a systematic analysis of IoT assaults, security issues, and reinforcement learning solutions.