How does Q-Learning handle exploration-exploitation under uncertainty in large state spaces?

Q-Learning relies on exploration strategies like epsilon-greedy, but struggles in large state spaces due to inefficient coverage.

In large state spaces, random exploration becomes ineffective because the probability of visiting rewarding states is extremely low. This leads to slow learning and poor policy discovery. Uncertainty-aware exploration methods, such as optimistic initialization, UCB-style exploration, or intrinsic motivation, can improve efficiency by guiding exploration toward underexplored but promising regions.