A Predictive Q-Learning Algorithm for Deflection Routing in Buffer-less Networks

In this paper, we introduce a predictive Q-learning deflection routing (PQDR) algorithm for buffer-less networks. Q-learning, one of the reinforcement learning (RL) algorithms, has been considered for routing in computer networks. The RL-based algorithms have not been widely deployed in computer networks where their inherent random nature is undesired. However, their randomness is sought-after in certain cases such as deflection routing, which may be employed to ameliorate packet loss caused by contention in buffer-less networks. We compare the proposed algorithm with two existing reinforcement learning-based deflection routing algorithms. Simulation results show that the proposed algorithm decreases the burst loss probability in the case of heavy traffic load while it requires fewer deflections. The PQDR algorithm is implemented using the ns-3 network simulator.