A target network is a type of neural network commonly used in the field of reinforcement learning, particularly in algorithms like Deep Q-Networks (DQN). Its primary purpose is to stabilize the training process by providing a more consistent set of value estimates for action selection.
In reinforcement learning, agents learn to make decisions by interacting with an environment and receiving feedback in the form of rewards. However, directly updating the value function (which predicts the expected future rewards of actions) can lead to oscillations and instability during training. To mitigate this issue, the target network is introduced.
The target network is typically a copy of the primary network (often called the online network) but is updated less frequently. During training, the online network is used to select actions and generate Q-values (value estimates), while the target network is used to calculate the target Q-values for updating the online network. This means that the target network provides a stable reference point for the updates, reducing the risk of drastic changes caused by fluctuating Q-value estimates.
To maintain stability, the target network is updated periodically, often by copying the weights from the online network after a fixed number of training steps. This approach helps ensure that the learning process is more stable, allowing the agent to converge to an optimal policy more effectively.
In summary, target networks play a crucial role in reinforcement learning by providing stability and consistency in value estimation, which is essential for the effective training of agents in dynamic environments.