How does reward scaling affect Q-Learning stability?

Reward scaling directly impacts gradient magnitude and training stability in Q-learning.

Large reward magnitudes can cause Q-value explosion and unstable updates, especially in deep Q-networks. Small rewards can lead to vanishing gradients and slow learning. Proper normalization or clipping ensures consistent scale of updates, improving convergence behavior.