Routing Algorithm Design Based on Deep Reinforcement Learning and GNN

We researched and designed the routing algorithms GDQR and GD3QR based on deep reinforcement learning and graph neural networks, and completed model training and per-formance testing. We designed the deep reinforcement learning routing algorithm reward function and derived the value learning theory used by the algorithm. Then based on the Markov decision process we designed, we researched and designed the decision-making and training parts of the intelligent routing algorithm, and proposed GDQR routing algorithm. Then, we improved the GDQR algorithm based on Dueling Network and Double DQN(Deep Q Learning) technology and proposed the GD3QR routing algorithm to address the problem of traffic fluctuations having a greater impact on the reward function in routing scenarios and the overestimation problem in value learning. We completed the training of the GDQR and GD3QR algorithms and tested the performance of the two algorithms. The results show that the GDQR and GD3QR algorithms can effectively adjust routing according to the current network status and improve the QoS performance of the entire network in complex traffic scenarios.