Integrated modular avionics systems primarily achieve system fault tolerance by reconfiguring the system configuration blueprints. In the design of manual reconfiguration, the quality of reconfiguration blueprints is influenced by various unstable factors, leading to a certain degree of uncertainty. The effectiveness of reconfiguration blueprints depends on various factors, including load balancing, the impact of reconfiguration, and the time required for the process. Solving high-quality reconfiguration configuration blueprints can be regarded as a type of multi-objective optimization problem. Traditional algorithms have limitations in solving multi-objective optimization problems. Multi-Agent Reinforcement Learning (MARL) is an important branch in the field of machine learning. It enables the accomplishment of more complex tasks in dynamic real-world scenarios through interaction and decision-making. Combining Multi-Agent Reinforcement Learning algorithms with reconfiguration techniques and utilizing MARL methods to generate blueprints can optimize the quality of blueprints in multiple ways. In this paper, an Improved Value-Decomposition Networks (VDN) based on the average sequential cumulative reward is proposed. By refining the characteristics of the integrated modular avionics system, mathematical models are developed for both the system and the reconfiguration blueprint. The Improved VDN algorithm demonstrates superior convergence characteristics and optimization effects compared with traditional reinforcement learning algorithms such as Q-learning, Deep Q-learning Network (DQN), and VDN. This superiority has been confirmed through experiments involving single and continuous faults.