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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2021-09-19


    Size :

    891245 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    CommonRoad-Reach: A Toolbox for Reachability Analysis of Automated Vehicles

    Liu, Edmond Irani / Wursching, Gerald / Klischat, Moritz et al. | IEEE | 2022





    Automatic Traffic Scenario Conversion from OpenSCENARIO to CommonRoad

    Lin, Yuanfei / Ratzel, Michael / Althoff, Matthias | IEEE | 2023