Route controlled autonomous vehicles could have a significant impact in reducing congestion in the future. Before applying multi-agent reinforcement learning algorithms to route control, we can model the system using a congestion game to predict and mitigate potential issues. We consider the problem of distributed operating systems in a transportation network that control the routing choices of their assigned vehicles. We formulate an associated network control game, consisting of multiple actors seeking to optimise the social welfare of their assigned subpopulations in an underlying nonatomic congestion game. Then we find the inefficiency of the routing equilibria by calculating the Price of Anarchy for polynomial cost functions. Finally, we extend the analysis to allow vehicles to choose their operating system.
翻译:道路控制的自治车辆在未来可以对减少拥堵产生重大影响。 在应用多试剂强化学习算法进行路线控制之前,我们可以使用阻塞游戏来模拟该系统,以预测和缓解潜在的问题。我们考虑在控制其指定车辆路线选择的运输网络中分布式操作系统的问题。我们设计了一个相关的网络控制游戏,由多个行为体组成,在一个基本的非原子拥堵游戏中,力求优化其指定亚群人口的社会福利。然后,我们通过计算多价成本功能的无政府状态价格,发现路径平衡效率低下。最后,我们扩大了分析范围,允许车辆选择其操作系统。