CommonRoad-RL: A Configurable Reinforcement Learning Environment for Motion Planning of Autonomous Vehicles

摘要

Reinforcement learning (RL) methods have gained popularity in the field of motion planning for autonomous vehicles due to their success in robotics and computer games. However, no existing work enables researchers to conveniently compare different underlying the Markov decision processes (MDPs). To address this issue, we present CommonRoad-RL-an open-source toolbox to train and evaluate RL-based motion planners for autonomous vehicles. Configurability, modularity, and stability of CommonRoad-RL simplify comparing different MDPs. This is demonstrated by comparing agents trained with different rewards, action spaces, and vehicle models on a real-world highway dataset. Our toolbox is available at commonroad.in.tum.de.