How does reward delay affect credit assignment in Q-Learning?

Delayed rewards make it difficult for Q-Learning to identify which actions contributed to success.

Credit assignment becomes harder when rewards are sparse or delayed because the algorithm must propagate reward signals backward across many time steps. Q-learning relies on temporal difference updates, which propagate reward gradually, but long delays slow learning significantly. Techniques like eligibility traces, reward shaping, and multi-step returns improve efficiency.