Sim-to-real transfer for reinforcement learning driving planners

Demonstrate successful sim-to-real transfer of reinforcement learning-based autonomous driving planning policies trained in simulation (e.g., CARLA and nuPlan) to real-world vehicles to establish practical relevance.

Background

The work establishes strong performance in simulation for reinforcement learning planners using a simple reward formulation and large-scale PPO training on CARLA and nuPlan. However, the authors acknowledge that real-world applicability hinges on demonstrating transfer from simulation to real cars.

They explicitly defer this step to future work, indicating that establishing effective sim-to-real transfer for RL planners remains unresolved.