Risk-Conscious Mutations in Jump-Start Reinforcement Learning for Autonomous Racing Policy
提出一种结合风险意识突变与跳跃启动强化学习的框架，用于自主赛车的轨迹规划与运动控制，在复杂高风险场景下提升训练效率并避免保守策略，实验证明其有效性和可扩展性。
This study focuses on trajectory planning and motion control policies in autonomous racing, which necessitates pushing the capacity boundaries of racing vehicles to achieve maximum speeds and minimal lap times. We propose an innovative planning control framework that integrates risk-conscious mutations in jump-start reinforcement learning (RCM-JSRL) and nonlinear model predictive control (NMPC). The RCM-JSRL algorithm incorporates jump-start curriculum learning and the risk-conscious genetic algorithm into reinforcement learning, leveraging prior expert knowledge and a curiosity-driven exploration mechanism to enhance training efficiency while avoiding excessively conservative policy generation in high-complexity and high-risk scenarios. NMPC generates locally optimal control commands that adhere to vehicle dynamics constraints while following the designated trajectory. Following training on track maps with varying difficulty levels, the proposed controller successfully executes a superior policy compared to the guide policy, providing evidence of its effectiveness and scalability. It is our belief that this technology can be applied in everyday driving scenarios, improving efficiency under special conditions, ensuring stability in critical situations, and broadening the scope of autonomous driving applications.