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首页> 外文会议>International Conference on Natural Computation;ICNC '09 >A Study of the Cooperation Control of Two Adjacent Intersections Based on N.B.S. Game Q-Learning Algorithm
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A Study of the Cooperation Control of Two Adjacent Intersections Based on N.B.S. Game Q-Learning Algorithm

机译：基于N.B.S.的两个相邻交叉口的协作控制研究游戏Q学习算法

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摘要

A new control algorithm -- N.B.S.Game Q-Learning was introduced to solve the cooperation control of two adjacent intersections in this paper. N.B.S.Game was denoted as two-player cooperation game with Nash Bargaining Solution. Based on Game Q-learning algorithm, that the game theory was combined with the Q-learning realized by BP neural network and the game solution was regarded as the basis of taking the strategy selecting of Q-learning, the N.B.S.Game Q-learning algorithm was just put forward. Because the traffic signal cooperation control problem for two adjacent intersections belonged to the two-player general sum cooperation game form, the Nash bargaining solution method was applied to obtain the optimal portfolio strategy to ensure the maximization of the overall benefit. The simulation result by Paramics has showed the control performance of the N.B.S.Game Q-learning algorithm is far better than fixed time control in heavy traffic flow condition and the control strategy can adapt to the variable traffic environment.
机译：引入了一种新的控制算法-N.B.S. Game Q-Learning来解决两个相邻路口的协调控制问题。 N.B.S. Game被Nash Bargaining Solution称为两人合作游戏。基于博弈Q学习算法，将博弈论与BP神经网络实现的Q学习相结合，以博弈解决方案作为采取Q学习策略选择的基础，NBSGame Q学习算法刚刚提出。由于两个相邻交叉口的交通信号协调控制问题属于两人通用和合作博弈形式，因此采用纳什讨价还价方法求解最优投资组合策略，以确保整体收益最大化。 Paramics的仿真结果表明，在交通繁忙的情况下，N.B.S。Game Q学习算法的控制性能远优于固定时间控制，并且该控制策略可以适应可变的交通环境。

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