Routing optimization method and system based on graph neural network and deep reinforcement learning

The invention discloses a routing optimization method and system based on a graph neural network and deep reinforcement learning, and belongs to the field of network routing optimization. The method comprises the following steps: measuring a current network state s, and selecting k shortest paths from a source node to a target node as an action set a according to a traffic demand distributed by a current network state request; inputting the action set a into the graph neural network, aggregating and iteratively updating link features , and obtaining a network state s and an estimated Q value of the action set a through a Q function; and performing deep reinforcement learning according to the estimated Q value to obtain a routing strategy in the current network state, and feeding back the routing strategy to the network topology to execute a corresponding routing action. The invention provides a network routing optimization system structure based on the graph neural network and deep reinforcement learning, and