How does Q-Learning behave when reward signals are delayed and noisy simultaneously?

Delayed and noisy rewards significantly slow learning and increase variance in Q-value updates.

When rewards are both delayed and noisy, Q-learning must propagate weak and uncertain signals across long time horizons. This increases variance in TD targets and slows credit assignment. The combination makes it difficult for the agent to distinguish signal from noise. Techniques like reward shaping, multi-step returns, and variance reduction methods become essential in such settings.