How does Q-Learning handle long-term dependency problems?

Q-Learning handles long-term dependencies via bootstrapping but struggles with very long horizons.

Long-term dependencies require propagating reward signals across many steps. Q-Learning uses temporal difference learning to gradually propagate rewards backward, but this process becomes inefficient in very long horizons. Techniques like eligibility traces, n-step returns, and recurrent architectures improve long-term credit assignment.