论文标题
基于状态辍学的课程增强学习在未信号交叉处进行自动驾驶
State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections
论文作者
论文摘要
对于自动驾驶汽车来说，遍历交叉点是一个具有挑战性的问题，尤其是当交叉口没有交通控制时。最近，由于其成功处理自动驾驶任务，深厚的强化学习受到了极大的关注。在这项工作中，我们解决了使用新颖的课程进行深入增强学习的问题的问题。拟议的课程导致：1）与未经课程训练的代理人相比，增强剂学习代理的更快的训练过程和2）表现更好。我们的主要贡献是两个方面：1）提供一种用于培训深入增强学习剂的独特课程，以及2）显示所提出的课程在未信号的交叉遍历任务中的应用。该框架期望自动驾驶汽车的感知系统对周围环境进行了处理。我们在COONMROAD运动计划模拟器中测试了我们的T交换和四向交集的方法。
Traversing intersections is a challenging problem for autonomous vehicles, especially when the intersections do not have traffic control. Recently deep reinforcement learning has received massive attention due to its success in dealing with autonomous driving tasks. In this work, we address the problem of traversing unsignalized intersections using a novel curriculum for deep reinforcement learning. The proposed curriculum leads to: 1) A faster training process for the reinforcement learning agent, and 2) Better performance compared to an agent trained without curriculum. Our main contribution is two-fold: 1) Presenting a unique curriculum for training deep reinforcement learning agents, and 2) showing the application of the proposed curriculum for the unsignalized intersection traversal task. The framework expects processed observations of the surroundings from the perception system of the autonomous vehicle. We test our method in the CommonRoad motion planning simulator on T-intersections and four-way intersections.
