Deep Q-Networks (DQN) often suffer from instability during t…

Deep Q-Networks (DQN) often suffer from instability during training due to correlated experience samples, rapidly changing target values, and high variance in updates. Several techniques have been introduced to improve learning stability and produce more reliable Q-value estimates. Which of the following are commonly used methods to stabilize learning in DQN? (Select two answers.)