Eco-approach and departure is a complex control problem wherein a driver’s actions are guided over a period of time or distance so as to optimize fuel consumption. Reinforcement learning (RL) is a machine learning paradigm that mimics human learning behavior, in which an agent attempts to solve a given control problem by interacting with the environment and developing an optimal policy. Unlike the methods implemented in previous studies for solving the eco-driving problem, RL does not require prior knowledge of the environment to be learned and processed. This paper develops a deep reinforcement learning (DRL) agent for solving the eco-approach and departure problem in the vicinity of signalized intersections for minimization of fuel consumption. The DRL algorithm utilizes a deep neural network for the RL. Novel strategies such as varying actions, prioritized experience replay, target network, and double learning were implemented to overcome the expected instabilities during the training process. The results revealed the significance of the DRL algorithm in reducing fuel consumption. Interestingly, the DRL algorithm was able to successfully learn the environment and guide vehicles through the intersection without red light running violation. On average, the DRL provided fuel savings of about 13.02% with no red light running violations.

生态接近和离开是一个复杂的控制问题，其中在一段时间或距离上引导驾驶员的动作以优化燃料消耗。强化学习（RL）是一种模仿人类学习行为的机器学习范式，其中，代理试图通过与环境交互并制定最佳策略来解决给定的控制问题。与以前的研究中解决生态驾驶问题的方法不同，RL不需要事先了解和处理环境。本文开发了一种深度强化学习（DRL）代理，用于解决信号交叉口附近的生态接近和驶离问题，以最大程度地减少燃料消耗。DRL算法将深度神经网络用于RL。新颖的策略，例如各种动作，实施了优先级的经验重播，目标网络和双重学习，以克服培训过程中预期的不稳定性。结果揭示了DRL算法在降低燃油消耗方面的重要性。有趣的是，DRL算法能够成功学习环境并引导车辆通过十字路口，而不会违反红灯行驶规定。平均而言，DRL节省了约13.02％的燃油，并且没有违反红灯行驶规定。DRL算法能够成功学习环境并引导车辆通过十字路口，而不会违反红灯行驶规定。平均而言，DRL节省了约13.02％的燃油，并且没有违反红灯行驶规定。DRL算法能够成功学习环境并引导车辆通过十字路口，而不会违反红灯行驶规定。平均而言，DRL节省了约13.02％的燃油，并且没有违反红灯行驶规定。