A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework
Propose a coordinated multi-agent reinforcement learning framework for signal control. Propose the Junction Tree Algorithm to obtain best joint actions of coordinated intersections. Various scenarios are constructed and multiple tests are conducted in the case study. Assess the delay, number of stops, and environmental impacts of the proposed controller. Results show that the proposed algorithm outperforms other learning based algorithms.
reinforcement learning junction tree signal control coordination vissim emissions moves