Highway monitoring method and system based on deep reinforcement learning algorithm

The invention provides an expressway monitoring method and system based on a deep reinforcement learning algorithm, and the method comprises the steps: determining whether to start a variable speed limit control strategy in an upstream region of a certain road section or not according to a comparison result of traffic capacity and a preset threshold value; a DDQN intelligent agent based on a deep reinforcement learning algorithm is used for monitoring the traffic state of each road section in an expressway, and a real-time neural network and a target neural network are used for controlling the variable speed limit of vehicles in each road section in the expressway. And the experience samples in the memory pool are repeatedly trained for multiple times to obtain an optimal speed limit value action, so that an optimal variable speed limit control strategy is obtained, and finally, the DDQN intelligent agent displays the optimal speed limit value. According to the method, the speed difference between the vehicle