Behaviorally-Aware Multi-Agent RL With Dynamic Optimization for Autonomous Driving

This study presents a novel Multi-Agent Reinforcement Learning (MURL) architecture for autonomous vehicle (AV) navigation in complex urban traffic environments. By integrating a Social Value Orientation (SVO) model into a model-free SARSA reinforcement learning framework, our approach effectively balances individual agents’ social preferences with safety and performance objectives. A logistic regression-based risk assessment module evaluates collision probabilities in real time by analyzing spatiotemporal dynamics such as distances and velocities. Additionally, a dynamic optimizer adapts the learning rate and exploration strategies of the SARSA algorithm to provide efficient convergence to optimal policies. Extensive simulation experiments demonstrate that the proposed method significantly enhances safety and efficiency, achieving a 55.6% reduction in collision risk and increasing average rewards per episode by 2.1 compared to traditional SARSA without SVO. Furthermore, the optimized policy reduces average episode length, indicating the framework’s effectiveness in providing robust decision-making and adaptability across various traffic scenarios.