RMSProp, or Root Mean Squared Propagation, is an adaptive learning rate optimization algorithm designed to improve the training of deep learning models. It addresses the limitations of traditional gradient descent methods, particularly in non-convex optimization problems. By maintaining a moving average of the squared gradients, RMSProp adjusts the learning rates for each parameter individually, allowing for more effective convergence. This method helps to mitigate issues like diminishing learning rates seen in algorithms like Adagrad, enabling faster and more stable training. RMSProp is widely used in various deep learning frameworks, including TensorFlow and Keras, due to its efficiency and adaptability.