CommonRoad-RL: A Configurable Reinforcement Learning Environment for Motion Planning of Autonomous Vehicles
CommonRoad-RL: A Configurable Reinforcement Learning Environment for Motion Planning of Autonomous Vehicles
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DOI：
10.1109/itsc48978.2021.9564898
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Xiao Wang;Hanna Krasowski;M. Althoff
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文献类型：
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作者：
Xiao Wang;Hanna Krasowski;M. Althoff
强化学习(RL)方法因其在机器人学和计算机游戏领域的成功而在自主车辆的运动规划领域获得了广泛的应用。然而，没有现有的工作使研究人员能够方便地比较不同的马尔可夫决策过程(MDP)。为了解决这一问题，我们提出了CommonRoad-RL-一个开源工具箱，用于培训和评估基于RL的自主车辆运动规划器。CommonRoad-RL的可配置性、模块化和稳定性简化了不同MDP的比较。这是通过比较在真实世界公路数据集上用不同奖励、动作空间和车辆模型训练的代理来演示的。我们的工具箱可以在CommonRoad.in.um.de上找到。
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.