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.
翻译:随着减少能源消耗和温室气体排放需求的不断增长,生态驱动战略为在运输部门寻求的其他技术解决办法之外进一步节省燃料提供了重要机会。本文件提议为积极的生态驱动援助提供一个无模式的深层强化学习控制剂(RL),将燃料消耗与其他驾驶-住宿目标进行交换,并从经验中学习最佳牵引和传输政策转移。拟议的生态驱动剂培训计划使用一种离政策行为者-批评结构,通过混合行动空间的最大事后政策优化算法,反复进行政策评价,并采用多步骤回报和政策改进。拟议的生态驱动RL代理器在交通后在一辆商用车辆上实施,这表明在最大限度减少燃料消耗方面业绩优于完全了解燃料效率表的基线控制器。