Optimising Microwave Darkroom Layout to Enhance Test Accuracy Using Reinforcement Learning

Semi-electrowave darkroom test accuracy is significantly affected by layout optimisation, and traditional methods rely on expert experience. In this paper, we propose a reinforcement learning-based optimization method for the layout of 3-meter semi-electrowave darkroom, design the state representation and reward function of electromagnetic environment specialization, and construct an intelligent optimization framework. The research integrates electromagnetic theory and artificial intelligence technology to achieve double optimisation of test accuracy and cost-effectiveness by optimising the distribution of wave-absorbing materials and the location of test equipment. The method provides a new way of artificial intelligence empowerment for the design of electromagnetic compatibility test facilities, which has theoretical value and application prospects.