增强学习与神经网络在交通信号控制中的应用

Application of reinforcement learning and neural network in traffic signal control.

城市交通系统是一个十分复杂的系统,鉴于交通流的多变性和交通控制的实时性,将增强学习应用于交通信号控制问题中,这样就可以根据实时的交通状态信息动态地进行决策,自动地适应环境以便取得更好的控制效果。然而由于交通状态空间太大而难以建立线性表,采用增强学习与人工神经网络相结合的方法,解决了多个路口的交通信号控制问题。通过在仿真环境下的对比,证明该方法的控制效果明显优于传统的固定配时控制策略。

Urban traffic system is very complicated.For the complexity of traffic flow and the real-time requirement of traffic control,Reinforcement Learning can be applied to the traffic signal control problem.It makes dynamic decisions depending on the real-time traffic information,and adapts to the environment automatically so as to achieve better results.As the state space is too huge to set up a linear table,this paper proposes a hybrid approach which is based on Reinforcement Learning and Artificial Neural Network to solve the traffic signal control problem of multi intersection.By comparing this approach with traditional fix sequence traffic signal control strategy in simulative environment,the result indicates that the new approach is better.