论文标题
通过通用通信方法进行交通信号控制的多机构强化学习
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method
论文作者
论文摘要
如何在实际复杂的流量情景中与多交流有效地协调交汇处之间的通信是具有挑战性的。现有方法只能以启发式方式进行沟通，而无需考虑要共享信息的内容/重要性。在本文中，我们提出了在交集之间的普遍通信形式。 Unicomm嵌入了一种在一个代理商中收集的大量观察结果，以对其对邻国的影响的重要预测，从而提高了沟通效率，并且在现有方法中是普遍的。我们还提出了一个简洁的网络UNILIGHT，以充分利用Unicomm启用的通信。实际数据集的实验结果表明，Unicomm普遍提高了现有最新方法的性能，并且在广泛的交通情况下，Unimight明显优于现有方法。
How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging. Existing approaches only enable the communication in a heuristic manner without considering the content/importance of information to be shared. In this paper, we propose a universal communication form UniComm between intersections. UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods. We also propose a concise network UniLight to make full use of communications enabled by UniComm. Experimental results on real datasets demonstrate that UniComm universally improves the performance of existing state-of-the-art methods, and UniLight significantly outperforms existing methods on a wide range of traffic situations.
