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, particularly in scenarios with non-convex loss surfaces. By maintaining a moving average of the squared gradients, RMSProp adjusts the learning rates for each parameter individually, allowing for more effective convergence. This approach helps to mitigate issues like aggressive learning rate decay seen in other methods, such as Adagrad, enabling faster and more stable training. RMSProp is particularly beneficial in optimizing complex neural networks, enhancing their performance and efficiency.