ReBound: Reset-Free Reinforcement Learning for Agile Driving via Reset-Aware Semi-Markov Bootstrapping
ReBound introduces a reset-free reinforcement learning method for agile driving that avoids manual resets after failures by alternating between a forward policy and a reset policy. It uses Model Predictive Path Integral control (MPPI) as both, allowing continuous training on physical platforms despite real-world noise and dynamics.