Urban Traffic Signal Learning Control Using Fuzzy Actor-Critic Methods

Abstract

Urban traffic control is very complicated, so it is very difficult to build a precise mathematical model. In this paper, we propose a fuzzy Actor-Critic reinforcement leaning algorithm to control the traffic signal, thus the decision can be made dynamically according to real-time traffic state information, and the change of environment can be adapted automatically; In order to solve the curse of the dimensionality problem, we applied fuzzy radial basis function (FRBF) neural network to approximate the state value function. By training self-adapted non-linear processing unit, and realizing online and adaptive constructing of state space, the approximation is improved, thus the control of traffic signal at single intersections is solved. The simulation results show that the effectiveness of the new control algorithm is obviously better than traditional sliced time allocation methods.