A. Deep Q-Networks (DQN) come with several limitations. They can suffer from instability during training due to the non-stationarity problem caused by frequent updates of the neural network. Additionally, DQNs might overestimate Q-values, impacting the learning process. They struggle with handling continuous action spaces and can be computationally expensive, requiring significant training time and resources. Exploration in high-dimensional state spaces can be challenging, leading to suboptimal policies. Lastly, tuning hyperparameters for DQNs can be intricate and sensitive, affecting convergence and overall performance. Despite these limitations, techniques like Double Q-learning and prioritized experience replay aim to address some of these challenges.