论文标题
驾驶员协助通过深入的增强学习的生态驾驶和传输控制
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
论文作者
论文摘要
随着减少能源消耗和温室气体排放的需求日益增长，生态驾驶策略为运输部门其他技术解决方案提供了额外节省的巨大燃料。在本文中，提出了一种无模型的深钢筋学习（RL）控制代理，以进行积极的生态驾驶援助，以将燃油消耗与其他驾驶员适应性目标进行交易，并从经验中学习最佳的牵引扭矩和变速箱转移政策。拟议的RL代理的培训方案使用了一个非政策的参与者批评体系结构，迭代性地进行了策略评估，并通过多步返回和策略改进以及最大的后验策略优化算法用于混合动作空间。拟议的生态驾驶RL代理是在交通后汽车上使用的商用车实施的。与具有完全了解燃油效率表的基线控制器相比，它在最小化燃油消耗方面表现出了卓越的性能。
With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of other technological solutions being pursued in the transportation sector. In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance that trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience. The training scheme for the proposed RL agent uses an off-policy actor-critic architecture that iteratively does policy evaluation with a multi-step return and policy improvement with the maximum posteriori policy optimization algorithm for hybrid action spaces. The proposed Eco-driving RL agent is implemented on a commercial vehicle in car following traffic. It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.
