Question

What is the purpose of using a target network in Deep Q-Networks (DQNs)?

Accepted Answer

The purpose of using a target network in Deep Q-Networks (DQNs) is to stabilize the training process and prevent oscillations. In DQNs, the Q-value of a state-action pair is updated based on the Bellman equation, which involves the maximum Q-value of the next state. In a standard DQN, the same network is used to estimate both the current Q-value and the target Q-value (the maximum Q-value of the next state). This can lead to instability because the target Q-value is constantly changing as the network learns, which can cause the training to oscillate or diverge. The target network addresses this issue by creating a separate copy of the Q-network, called the target network, which is used to estimate the target Q-values. The target network&#x27;s weights are updated periodically, typically by copying the weights from the main Q-network every few steps. This means that the target Q-values remain relatively stable over short periods, reducing the variance in the updates and stabilizing the training process. For example, if a DQN is learning to play a video game, the target network would provide a more stable estimate of the expected reward for taking a certain action, preventing the network from chasing constantly changing targets. Using a target network is a crucial technique in DQNs, enabling them to learn stable and effective policies in complex environments.

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What is the purpose of using a target network in Deep Q-Networks (DQNs)?

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