What is gradient explosion in Deep Q-Networks and how is it controlled?

Gradient explosion occurs when updates become too large, destabilizing Q-network training.

In DQN, large TD-errors or high reward variance can cause gradients to grow exponentially. This leads to unstable parameter updates and divergence. Techniques like gradient clipping, reward normalization, and Huber loss are used to stabilize training.