Explain Q-learning and Deep Q-Networks (DQN).

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🔹 Q-Learning

• Definition: Q-learning is a model-free reinforcement learning (RL) algorithm used to learn the optimal action-selection policy for an agent.

• Idea: The agent interacts with the environment, receives rewards, and learns which actions maximize cumulative reward.

• Q-Table: It stores Q-values (state–action values) that estimate how good an action is in a given state.

Q-value update rule:

$$Q(s,a) \leftarrow Q(s,a) + \alpha \Big[ r + \gamma \max_{a'} Q(s',a') - Q(s,a) \Big]$$

• s = current state

• a = action taken

• r = reward received

• s' = next state

• γ = discount factor (future reward importance)

• α = learning rate

✅ Works well for small state-action spaces, but struggles with large or continuous spaces because the Q-table becomes huge.

🔹 Deep Q-Networks (DQN)

• Problem with Q-learning: For complex environments (like video games), storing a Q-table is impossible.

• Solution: Replace the Q-table with a neural network that approximates Q-values.

• Input = state, Output = Q-values for all possible actions.

Key Innovations in DQN (by DeepMind, 2015):

1. Experience Replay: Store past experiences (state, action, reward, next_state) in memory and sample them randomly to break correlation and stabilize training.

2. Target Network: Maintain a separate copy of the Q-network (target network) to compute stable Q-value targets, updated less frequently.

3. End-to-End Learning: Can learn directly from raw pixels (e.g., playing Atari games from screen images).

✅ DQN made reinforcement learning practical for high-dimensional problems like games, robotics, and autonomous systems.

⚖️ Quick Difference

• Q-learning: Uses a table → good for small, discrete environments.

• DQN: Uses a deep neural network → scalable to large/complex environments.

Read More:

What are GANs (Generative Adversarial Networks), and how do they work?

What are benchmark datasets in AI testing?

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