RMSProp, or Root Mean Squared Propagation, is an adaptive learning rate optimization algorithm widely used in training deep learning models. It addresses the limitations of traditional gradient descent methods by adjusting the learning rate based on the moving average of the squared gradients. This allows RMSProp to maintain a more stable and efficient learning process, particularly in scenarios with non-stationary objectives. By preventing the learning rate from decreasing too aggressively, as seen in algorithms like Adagrad, RMSProp enables faster convergence and improved performance in optimizing complex neural networks. Its flexibility makes it a popular choice among practitioners in machine learning.