Fuzzy Graph and Collective Multiagent Reinforcement Learning for Traffic Signals Control

Multiagent systems provide proper modeling in real-world applications such as intelligent transportation systems. The interaction between the agents can be represented by the graph theory. In this article, a fuzzy graph is used for urban traffic network modeling. A network composed of several intersections is considered as a multiagent system composed of multiple interacting agents. The interaction between the agents can be represented by a fuzzy graph in which each vertex shows an agent in the network. The network is divided into correlated agent's sets. In each set, collective learning composed of Q-learning and function approximation method is used to learn the optimal control policy. The total average energy of the sets of correlated agents as fuzzy subgraphs is computed and the relationship between these values and the effectiveness of the collective learning is studied. Experimental results show that the proposed collective learning method leads to better results compared to the independent mode in which each agent controls the intersection individually. In addition, the energy of fuzzy subgraphs related to the set of correlated agents are computed and the dependence of the energy and effectiveness of the collective learning method is studied in the results.

Automated summary

This article models urban traffic networks using fuzzy graphs and implements collective learning of related agent sets using Q-learning and function approximation methods. The relationship and effectiveness of collective learning methods are studied and compared to independent control modes for better results.