What is the role of initialization in deep Q-network generalization?

Initialization influences early exploration behavior and long-term generalization in DQNs.

In deep Q-networks, weight initialization affects gradient flow and early policy bias. Poor initialization can lead to dead neurons or biased Q-estimates, while good initialization encourages balanced exploration of actions. It indirectly impacts convergence speed and final policy quality.