Urban Traffic Signal Learning Control Using SARSA Algorithm Based on Adaptive RBF Network
Urban traffic control is very complicated, so to build a precise mathematical model for it is very difficult. In this paper, we use the SARSA 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; As the state space is too big to be stored and expressed directly, we applied radial basis function 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 and 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.
SARSA algorithm function approximation adaptive RBF neural network traffic signal learning control
Li Chun-gui Wang Meng Yang Shu-Hong Zhang Zeng-fang
Department of Computer Engineering Guangxi University of Technology Liuzhou, China
国际会议
张家界
英文
658-661
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）