论文标题
深入强化学习的自适应交通控制：朝着最新的及时进行
Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond
论文作者
论文摘要
在这项工作中，我们使用强化学习（RL）研究自适应数据引导的交通计划和控制。我们从对Deep RL社区的最先进方法的清晰使用转变为最先进的方法。我们将最近的几种技术嵌入了我们的算法中，这些技术改善了原始的深Q-Networks（DQN）进行离散控制，并讨论了随后的交通相关解释。我们提出了一种基于DQN的新型算法，用于流量控制（称为TC-DQN+），作为快速，更可靠的交通决策的工具。我们介绍了一种新的奖励功能形式，它使用说明性示例将其与传统的交通控制方法进行了比较。
In this work, we study adaptive data-guided traffic planning and control using Reinforcement Learning (RL). We shift from the plain use of classic methods towards state-of-the-art in deep RL community. We embed several recent techniques in our algorithm that improve the original Deep Q-Networks (DQN) for discrete control and discuss the traffic-related interpretations that follow. We propose a novel DQN-based algorithm for Traffic Control (called TC-DQN+) as a tool for fast and more reliable traffic decision-making. We introduce a new form of reward function which is further discussed using illustrative examples with comparisons to traditional traffic control methods.
