Theory of Reinforcement Learning and Algorithms of Route Choice in Transportation Networks

该研究表明，在交通网络中的个人旅行者被严格建模为自适应学习代理人谁接收的旅行信息，通过日常的日常经验，使他的决定，以加强他的行动依赖于实现的回报。提出了一种与理论相一致的自适应学习算法，并证明了该算法使系统以概率1到达纳什均衡。所提出的算法进行了数值测试，使用的例子网络与各种定义不明确的链路成本函数，并检查算法的快速收敛。此外，我们还提出了一种估计方法的结构参数中包含的路径选择模型。应用于室内实验获得的日常路径选择数据，效果令人满意。

This research shows that an individual traveler in transportation networks is rigorously modeled as an adaptive learning agent who receives travel information through day-to-day experience and makes his decision so as to reinforce his action depending the realized payoffs. An adaptive learning algorithm consistent with the theory is proposed and proved that it leads the system to a Nash equilibrium with probability one. The proposed algorithms have tested numerically by using example networks with various ill-defined link cost functions and examined a rapid convergence of the algorithms. In addition, we have proposed an estimation method for the structure parameters included in the route choice model. The application to the data of theday-to-day route choice obtained by the indoor experiments was satisfactory.