A target network is a stabilizing neural network used in Deep Q-learning to provide reference Q-values or policy estimates for training a main network. By separating the source of training targets from the network being updated, it reduces harmful feedback loops and improves learning stability.
In Deep Q-learning, a neural network that is a stable approximation of the main neural network, where the main neural network implements either a Q-function or a policy. Then, you can train the main network on the Q-values predicted by the target network. Therefore, you prevent the feedback loop that occurs when the main network trains on Q-values predicted by itself. By avoiding this feedback, training stability increases.