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

TL;DR

This empirical study investigates reset-free reinforcement learning for real-world agile driving using a scaled vehicle, highlighting challenges in sim-to-real transfer and comparing multiple RL algorithms with residual learning.

Contribution

It systematically compares RL algorithms in real-world agile driving, revealing the limitations of residual learning and the gap between simulation and real-world performance.

Findings

SAC with residual learning performs best in simulation.

TD-MPC2 outperforms MPPI baseline on the physical platform.

Residual learning benefits do not transfer well from simulation to real-world.

Abstract

This paper presents an empirical study of reset-free reinforcement learning (RL) for real-world agile driving, in which a physical 1/10-scale vehicle learns continuously on a slippery indoor track without manual resets. High-speed driving near the limits of tire friction is particularly challenging for learning-based methods because complex vehicle dynamics, actuation delays, and other unmodeled effects hinder both accurate simulation and direct sim-to-real transfer of learned policies. To enable autonomous training on a physical platform, we employ Model Predictive Path Integral control (MPPI) as both the reset policy and the base policy for residual learning, and systematically compare three representative RL algorithms, i.e., PPO, SAC, and TD-MPC2, with and without residual learning in simulation and real-world experiments. Our results reveal a clear gap between simulation and real-world: SAC with residual learning achieves the highest returns in simulation, yet only TD-MPC2 consistently outperforms the MPPI baseline on the physical platform. Moreover, residual learning, while clearly beneficial in simulation, fails to transfer its advantage to the real world and can even degrade performance. These findings reveal that reset-free RL in the real world poses unique challenges absent from simulation, calling for further algorithmic development tailored to training in the wild.