What is the role of normalization in stabilizing Q-value predictions?

Normalization stabilizes Q-learning by keeping inputs and rewards within consistent ranges.

Without normalization, large variations in input features or reward scales can destabilize training and cause exploding Q-values. Techniques like reward normalization, batch normalization, and advantage scaling help maintain stable gradients and improve convergence reliability.