Abstract:

Aiming at the routing optimization problem of different network loads under the same network topology, based on the deep reinforcement learning method, two optimization methods for routing distribution based on the current network traffic state are proposed. Through the iterative interaction between the network simulation system and the deep reinforcement learning model, continuous training and optimization of network routing for the distribution of traffic relationships are realized. Improvements have been made in using the deep deterministec policy gradient (DDPG) algorithm to solve the routing optimization problem, making this optimization method more suitable for solving the problem of network routing optimization. At the same time, a brand-new link weight construction strategy is designed, which uses network traffic to construct input state elements for the neural network. Through the preprocessing of the original data, the learning efficiency of the neural network is strengthened, and the stability of the training model is greatly improved. And for the continuous action space of the high-latitude large-scale network, the action space is discretized, which effectively reduces the complexity of the action space and speeds up the model convergence. Experimental results show that the proposed optimization method can adapt to changing traffic and link status, enhance the stability of model training and improve network performance.

Key words: deep reinforcement learning, routing optimization, deep deterministec policy gradient (DDPG) algorithm