Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization

ArXi:2603.12960v1 Announce Type: cross Residual policy learning (RPL), in which a learned policy refines a static base policy using deep reinforcement learning (DRL), has shown strong performance across various robotic applications. Its effectiveness is particularly evident in autonomous racing, a domain that serves as a challenging benchmark for real-world DRL. However, deploying RPL-based controllers