Enhancing Adversarial Robustness via Adaptive Hyperparameter Optimization and Generative Perturbation Synthesis in Deep Reinforcement Learning for Autonomous Vehicle Exploitation

**Abstract:** This paper introduces a novel framework, Adaptive Hyperparameter Optimization and Generative Perturbation Synthesis (AHOP-GPS), to enhance the adversarial robustness of Deep Reinforcement Learning (DRL) agents controlling autonomous vehicles (AVs). Current adversarial attacks targeting AVs often exploit vulnerabilities in the DRL agent's perception and control modules. AHOP-GPS addresses this by dynamically adapting DRL hyperparameter configurations during adversarial training and synthesizing highly effective, innovative perturbations using generative models. This approach results in significantly improved resilience to unseen adversarial attack vectors and enhances the reliability of AV systems deployed in complex, dynamic environments. Our methodology goes beyond static adversarial training, proactively fortifying the DRL agent against adaptive and evolving attack strategies. Crucially, AHOP-GPS focuses on achieving a practical balance between robustness and performance, avoiding the significant accuracy degradation frequently observed with conventional adversarial training techniques. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*