论文标题
高速公路上自动驾驶汽车的决策：通过连续动作的深度加强学习
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
论文作者
论文摘要
自动驾驶汽车的决策策略会取消一系列驾驶操作，以实现一定的导航任务。本文利用深入的增强学习方法（DRL）方法来解决高速公路上连续的摩尼斯决策问题。首先，引入了高速公路上的车辆运动学和驾驶场景。自动车辆的运行目标是执行高效且平稳的政策而不会发生冲突。然后，说明了名为近端策略优化（PPO）增强DRL的特定算法。为了克服迟到的训练效率和样本效率低下的挑战，该应用算法可以实现高学习效率和出色的控制效果。最后，从多个角度估算了基于PPO-DRL的决策策略，包括最佳，学习效率和适应性。通过将其应用于类似的驾驶场景来讨论其在线申请的潜力。
Decision-making strategy for autonomous vehicles de-scribes a sequence of driving maneuvers to achieve a certain navigational mission. This paper utilizes the deep reinforcement learning (DRL) method to address the continuous-horizon decision-making problem on the highway. First, the vehicle kinematics and driving scenario on the freeway are introduced. The running objective of the ego automated vehicle is to execute an efficient and smooth policy without collision. Then, the particular algorithm named proximal policy optimization (PPO)-enhanced DRL is illustrated. To overcome the challenges in tardy training efficiency and sample inefficiency, this applied algorithm could realize high learning efficiency and excellent control performance. Finally, the PPO-DRL-based decision-making strategy is estimated from multiple perspectives, including the optimality, learning efficiency, and adaptability. Its potential for online application is discussed by applying it to similar driving scenarios.
