Architecture Design and Optimization of Intelligent Payment Routing System Based on Reinforcement Learning

Given the dynamic transaction costs, changing delays, and fluctuating channel availability, multi-channel payment systems are facing increasing challenges in terms of routing efficiency. Traditional static routing strategies have poor adaptability to these heterogeneous and time sensitive environments. This study proposes an intelligent routing architecture based on reinforcement learning (RL), specifically tailored for real-time payment networks. We have developed a system model system, in which each payment channel is characterized by a set of dynamic attributes, namely cost, delay, and available capacity. Through the Deep Q-Network (DQN) framework, the routing agent learns the optimal decision. The intention of the reward function is to achieve a balance between transaction success rate, cost, and delay through adjustable weights. With the help of a customizable simulation platform and calibration based on real-world parameters in the Lightning Network benchmark dataset, we conducted extensive experiments on over 1000 transaction events. The proposed RL strategy outperforms both fixed and random baselines, achieving a success rate of 93.5%. During this process, the average cost and delay are significantly reduced. These findings support the robustness and adaptability of RL based routing models, especially when in unstable channel conditions, opening up a potential approach for next-generation payment infrastructure.