论文标题
驾驶任务在深入的强化学习中转移，以实现自动驾驶汽车的决策
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles
论文作者
论文摘要
知识转移是一个有前途的概念，可以实现自动驾驶汽车实时决策。本文构建了转移深的增强学习框架，以改变截面环境中的驾驶任务。非信号交叉路口的驾驶任务被施放在左转，右转弯，并直行以自动车辆奔跑。自治自我车辆（AEV）的目标是有效，安全地驾驶交叉路口。该目标促进了研究的车辆以提高其速度并避免撞击其他车辆。从一个驾驶任务中学到的决策政策是在另一个驾驶任务中转移和评估的。仿真结果表明，与类似任务相关的决策策略是可以转移的。它表明提出的控制框架可以减少时间消耗并实现在线实施。
Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning framework to transform the driving tasks in inter-section environments. The driving missions at the un-signalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making pol-icy learned from one driving task is transferred and evaluated in another driving mission. Simulation results reveal that the decision-making strategies related to similar tasks are transferable. It indicates that the presented control framework could reduce the time consumption and realize online implementation.
