Target networks in Deep Q-Networks (DQN) are a stabilization technique used to address the instability that arises when training a neural network to approximate Q-values in reinforcement learning. In DQN, the agent learns a policy by updating a neural network (the “online network”) to predict the expected future rewards (Q-values) for actions in a given state. However, because the same network is used to estimate both current and future Q-values during training, the targets for these predictions can change rapidly, leading to feedback loops and unstable learning. A target network is a separate, slower-updating copy of the online network that generates more stable Q-value targets for training, reducing this volatility.
The primary reason target networks are necessary is to decouple the estimation of current and target Q-values. Without a target network, the online network’s updates would immediately affect the calculation of future Q-values, creating a moving target. For example, if the online network’s weights change after each training step, the Q-value it predicts for the next state (used in the Bellman equation) will also shift, making it harder for the network to converge. By introducing a target network—a copy of the online network that updates its weights less frequently (e.g., every 1,000 training steps)—the targets remain fixed for multiple updates. This stabilizes the learning process, similar to how using a fixed reference point in optimization can prevent oscillations.
A practical example of target networks in action is their use in the original DQN algorithm for playing Atari games. Here, the online network is updated at every step using experiences from a replay buffer, while the target network’s weights are copied from the online network periodically. Another variation, used in algorithms like DDPG, employs a “soft update” mechanism, where the target network’s weights are gradually blended with the online network’s weights using a parameter like tau (e.g., τ = 0.01). This approach avoids abrupt changes and maintains smoother target value transitions. Without target networks, DQN often fails to learn effective policies, as the Q-value estimates diverge due to rapidly shifting targets. By decoupling the target generation from the immediate updates, target networks enable more reliable convergence in complex environments.