Reinforcement Learning for Adaptive Traffic Rule Compliance in Autonomous Driving Systems: A Multi-agent Framework for Dynamic Regulatory Adaptation

This article investigates the application of reinforcement learning for adaptive traffic rule compliance in autonomous driving systems. Current rule-based approaches lack flexibility in handling unpredictable driving scenarios and varying regulatory requirements across jurisdictions. This article proposes a novel multi-agent reinforcement learning framework that enables self-driving vehicles to dynamically adjust their behavior to different traffic rules while optimizing for safety, efficiency, and legal compliance. It integrates deep reinforcement learning techniques, specifically Proximal Policy Optimization and Multi-Agent Deep Q-Networks, with real-time rule validation modules to create adaptive driving policies. It allows autonomous vehicles to learn optimal behaviors through environmental interaction across diverse traffic conditions. Extensive simulation testing demonstrates that our reinforcement learning-based system consistently outperforms traditional rule-based and supervised learning approaches in compliance rates while maintaining smooth traffic flow. This article indicates significant potential for reinforcement learning to enhance the adaptability and robustness of autonomous driving systems in complex regulatory environments.