While the Deep Q-Network (DQN) algorithm provides a strong foundation for applying neural networks to reinforcement learning tasks, its original formulation can be improved. A notable issue is the tendency for standard DQN to overestimate action values, $Q(s, a)$, which can sometimes lead to suboptimal policies and instability during training.

This chapter addresses these limitations by introducing key enhancements to the DQN framework. You will learn:

• The reasons behind the Q-value overestimation problem inherent in Q-learning and standard DQN.
• How Double DQN (DDQN) modifies the update rule to decouple action selection from action evaluation, reducing overestimation bias.
• The concept behind Dueling Network Architectures, which separate the estimation of the state value function $V(s)$ and the action advantage function $A(s, a)$.
• How these improvements can be combined and gain a brief overview of techniques like Prioritized Experience Replay.

By the end of this chapter, you will understand how these variants build upon the original DQN to create more stable and effective agents, and you will practice implementing Double DQN.