Reinforcement Learning with TensorFlow

The exploding gradient problem

The exploding gradient problem is another problem associated with the training of artificial neural networks when the learning of the neurons present in the early layers diverge because the gradients become too large to cause severe changes in weights avoiding convergence. This generally happens if weights are not assigned properly.

While following the steps mentioned for the vanishing gradient problem, we observe that the gradients explode in the early layers, that is, they become larger. The phenomenon of the early layers diverging is called the exploding gradient problem.