Data Transmission Evaluation and Allocation Mechanism of the Optimal Routing Path: An Asynchronous Advantage Actor-Critic (A3C) Approach

The delay tolerant networks (DTN), which have special features, differ from the traditional networks and always encounter frequent disruptions in the process of transmission. In order to transmit data in DTN, lots of routing algorithms have been proposed, like “Minimum Expected Delay,” “Earliest Delivery,” and “Epidemic,” but all the above algorithms have not taken into account the buffer management and memory usage. With the development of intelligent algorithms, Deep Reinforcement Learning (DRL) algorithm can better adapt to the above network transmission. In this paper, we firstly build optimal models based on different scenarios so as to jointly consider the behaviors and the buffer of the communication nodes, aiming to ameliorate the process of the data transmission; then, we applied the Deep Q-learning Network (DQN) and Advantage Actor-Critic (A3C) approaches in different scenarios, intending to obtain end-to-end optimal paths of services and improve the transmission performance. In the end, we compared algorithms over different parameters and find that the models build in different scenarios can achieve 30% end-to-end delay decline and 80% throughput improvement, which show that our algorithms applied in are effective and the results are reliable.