Self-adaption traffic signal control system and method based on deep reinforcement learning

Abstract

The invention belongs to the field of intelligent traffic, and provides a self-adaption traffic signal control system and method based on deep reinforcement learning. According to the self-adaption traffic signal control system and method based on deep reinforcement learning, real-time interaction of the intersection environment and a controller is achieved by using an interaction module, namely the traffic state of an intersection is collected in real time by a state sensing module, and an optimal decision scheme of the present traffic state is given through a control decision module; and meanwhile, a control core (Q value network ) in the controller can be continuously updated by adopting a framework of reinforcement learning through an update module, and thus the optimal effect of a future control scheme is improved. According to the self-adaption traffic signal control system and method based on deep reinforcement learning, various influencing factors can be synthetically collectedin both dimensions of time and space; a recurrent neural network is used for improving the extraction capability and the generalization capability of characteristics of a high-dimensional input matrix; and the requirements of complexity, instantaneity, dynamics, randomness, adaption and the like in self-adaption traffic signal control can be met, the traffic control efficiency in the intersectionis improved, and travel delaying is reduced.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to an adaptive traffic signal control system and method based on deep reinforcement learning. Background technique [0002] With the deepening of China's urbanization process, the urban population and vehicles continue to increase, so urban traffic management needs to propose an adaptive urban traffic signal control method that can meet the dynamic needs. The salient features of the urban traffic system are: the dynamic fluctuation of traffic demand, the instability of time and space, the diversity of influencing factors, and the complexity of control strategies. [0003] The adaptive control methods in the prior art mostly adopt control system methods such as fuzzy control, neural network, and genetic algorithm. The adaptive control scheme in the prior art has the following characteristics: due to calculation conditions and modeling reasons, most adaptive control The sche...

AI Technical Summary

Problems solved by technology

However, the adaptive signal control scheme represented by Q-learning usually uses artificial feature variables as the traffic state, which simplifies the complexity of expressing the traffic state and cannot fully consider various potential information of the traffic state; secondly, the core of Q-learning is the state -The mapping relationship of the behavior value table leads to Q-learning leading to a large state space, low learning efficiency, and low efficiency of control strategy

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