A. The key difference between deep Q-learning and regular Q-learning lies in their approaches to function approximation. Regular Q-learning uses a table to store Q-values for each state-action pair, making it suitable for discrete state and action spaces. In contrast, deep Q-learning employs a deep neural network to approximate Q-values, enabling it to handle continuous and high-dimensional state spaces. While regular Q-learning guarantees convergence, deep Q-learning's convergence is less assured due to non-stationarity issues caused by updates to the neural network during learning. Techniques like experience replay and target networks are used to stabilize deep Q-learning training.