What is Dueling DQN?

Dueling DQN is an improvement of the Deep Q-Network (DQN) algorithm in reinforcement learning. The main idea is that the action quality estimate (Q-value) is decomposed into two parts:

• State value (Value function) — how good the current state is regardless of the action.
• Advantage function — how beneficial it is to choose a specific action in this state.

The Dueling DQN architecture has two separate branches in the neural network that evaluate these components, and then combine them to obtain the final Q-value. This helps better assess the importance of states and actions, especially in situations where the choice of action does not significantly affect the outcome.

This approach improves the stability and efficiency of learning, allowing the agent to find the optimal strategy faster.