Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study

We present ReBound (reset-aware semi-Markov bootstrapping), a value-learning method for reset-free reinforcement learning (RL) that learns agile driving through real-world RL without human intervention after training starts. Reset-free RL explores by alternating between a forward policy that performs the task and a reset policy that restores a drivable state. Standard reset-free RL treats collisions as episode terminations in value-function bootstrapping, truncating the return at each collision. This conflicts with the reset-free objective, under which driving resumes after recovery, and acts as a hidden collision penalty absent from the reward function. ReBound instead applies semi-Markov bootstrapping: it treats the collision-causing action and the subsequent recovery as a single semi-Markov transition and bootstraps from the post-recovery value, discounted by the actual recovery time. We further apply reward centering to stabilize value learning in the resulting continuing task. In simulation and on a 1/10-scale vehicle, ReBound substantially outperforms conventional value-learning methods and model-based control.