Chapter 1 highlighted the challenges of applying basic Q-learning and SARSA to problems with vast or continuous state spaces. Storing a Q-value for every possible state-action pair becomes computationally infeasible and prevents generalization to unseen states.

To overcome this scaling issue, we turn to function approximation. This chapter introduces Deep Q-Networks (DQN), a significant development that utilizes deep neural networks to estimate the action-value function, $Q(s, a)$. This allows RL agents to learn effectively in environments with high-dimensional inputs, like images from a game screen.

We will examine how neural networks replace the traditional Q-table and the specific techniques required to make this combination stable and effective. You will learn about:

• The fundamental concept of using neural networks for Q-value approximation.
• The core architecture and update mechanism of the DQN algorithm.
• Key techniques for training stability: Experience Replay and Target Networks.
• The loss function tailored for training DQNs.
• A step-by-step guide to implementing a basic DQN agent.

By completing this chapter, you will understand the operational principles of DQN and gain practical experience by building one for a standard RL environment.