• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IFAC PapersOnLine >A Deep Reinforcement Learning Approach for Autonomous Highway Driving
【24h】

A Deep Reinforcement Learning Approach for Autonomous Highway Driving

机译：自主公路驾驶深度加固学习方法

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Autonomous driving has been the trend. In this paper, a Deep Reinforcement Learning (DRL) method is exploited to model the decision making and interaction between vehicles on highway driving. To avoid the overestimate action values induced by Q-learning, we use the Double Deep Q-Network (DDQN) for the training of the host vehicle. The agent learns from trial and interactions with the environment. A simulation platform based on the Simulation of Urban Mobility (SUMO) is also established, it helps facilitate the variation of control algorithms. The results show that the proposed framework can simulate highway driving, and the trained agent can accomplish the driving task with ease after training and can approximate the highest safe driving speed as defined without collision.
机译：自主驾驶一直是趋势。 本文采用了深度加强学习（DRL）方法，以模拟公路驾驶车辆之间的决策和相互作用。 为避免Q-Learning引起的高估动作值，我们使用双层Q-Network（DDQN）来训练主机。 代理商从试验和与环境的互动中学习。 还建立了基于城市移动性（SUMO）仿真的仿真平台，有助于促进控制算法的变化。 结果表明，建议的框架可以模拟公路驾驶，训练代理可以在训练后轻松完成驾驶任务，并且可以近似于定义而无碰撞的最高安全驱动速度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号