Double DQN (Deep Q-Network) improves Q-learning by addressing a critical flaw in traditional DQN: overestimation of Q-values. In standard DQN, the same network is used to select and evaluate the best action for the target Q-value calculation, which can lead to overly optimistic value estimates. This happens because the max operator in the Q-learning update amplifies errors in the network’s predictions. Double DQN decouples action selection and evaluation by using two networks: the online network chooses the action, while the target network estimates its value. This separation reduces bias, resulting in more accurate Q-value updates.
The core improvement lies in how Double DQN computes the target Q-value. In standard DQN, the target is calculated as ( r + \gamma \cdot \max_{a’} Q(s’, a’; \theta^{-}) ), where ( \theta^{-} ) represents the target network. Double DQN modifies this to ( r + \gamma \cdot Q(s’, \text{argmax}_{a’} Q(s’, a’; \theta); \theta^{-}) ). Here, the online network ( \theta ) selects the best action for the next state ( s’ ), and the target network ( \theta^{-} ) evaluates that action’s value. For example, if the online network incorrectly favors a suboptimal action due to noise, the target network’s evaluation acts as a corrective filter, preventing the overestimation from propagating into training. This approach is inspired by Double Q-learning but adapted for neural networks.
Practically, Double DQN requires minimal changes to a standard DQN implementation. The target network update rule remains the same (e.g., periodic synchronization with the online network), but the target calculation logic is adjusted. Experiments in environments like Atari games demonstrate that Double DQN often achieves better performance with similar computational cost. For instance, in games where actions have similar long-term rewards, Double DQN’s reduced overestimation helps the agent learn more stable policies. By mitigating one of DQN’s key weaknesses, Double DQN provides a straightforward yet effective upgrade for developers aiming to improve reinforcement learning agents.