Navigation apps have become more and more popular, as they give information about the current traffic state to drivers who then adapt their route choice. In commuting scenarios, where people repeatedly travel between a particular origin and destination, people tend to learn and adapt to different situations. What if the experience gained from such a learning task is shared via an app? In this paper, we analyse the effects that adaptive driver agents cause on the overall network, when those agents share their aggregated experience about route choice in a reinforcement learning setup. In particular, in this investigation, Q-learning is used and drivers share what they have learnt about the system, not just information about their current travel times. Using a classical commuting scenario, we show that experience sharing can improve convergence times that underlie a typical learning task. Further, we analyse individual learning dynamics to get an impression how aggregate and individual dynamics are related to each other. Based on that interesting pattern of individual learning dynamics can be observed that would otherwise be hidden in an only aggregate analysis.

中文翻译：

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在通勤场景中通过经验共享来加速路线选择学习：基于代理的方法

导航应用程序变得越来越流行，因为它们向驾驶员提供有关当前交通状况的信息，然后他们会调整其路线选择。在通勤场景中，人们在特定的出发地和目的地之间反复旅行，人们倾向于学习并适应不同的情况。如果通过应用共享从这样的学习任务中获得的经验怎么办？在本文中，我们分析了自适应驾驶员代理在强化学习设置中共享其关于路线选择的综合经验时，对整个网络造成的影响。尤其是，在这项调查中，使用了Q学习，并且驾驶员分享了他们对系统的了解，而不仅仅是他们当前行驶时间的信息。使用经典的通勤场景，我们表明，经验共享可以缩短作为典型学习任务基础的收敛时间。此外，我们分析了个体的学习动态，以给人一种印象，即聚合和个体动态如何相互关联。基于这种有趣的个体学习动态模式，否则将仅在汇总分析中将其隐藏。