What is the role of initialization in Q-Learning convergence?

Initialization affects exploration behavior and convergence speed in Q-Learning.

Q-values or network weights initialized too optimistically can encourage exploration (optimistic initialization), while pessimistic initialization may discourage exploration. In tabular Q-learning, initializing Q-values high encourages agents to explore unseen actions. In deep Q-learning, weight initialization affects gradient flow, stability, and early policy formation.