What is overestimation bias correction in modern Q-Learning?

Modern Q-learning reduces overestimation using techniques like Double Q-Learning and clipped double estimators.

Overestimation occurs due to max operator on noisy estimates. Modern approaches decouple selection and evaluation (Double DQN), or use ensemble methods and clipped targets (as in TD3-style ideas adapted to Q-learning variants). This improves policy reliability and reduces optimistic drift.