What are Q-learning and deep Q-networks (DQN)?

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Q-learning is a popular model-free reinforcement learning algorithm used to help an agent learn the best action to take in a given state to maximize long-term rewards. It uses a function called the Q-value (action-value), which estimates the expected cumulative reward of taking an action in a specific state and then following the optimal policy. The Q-values are updated iteratively using the Bellman equation:

$$Q(s,a) = Q(s,a) + \alpha [r + \gamma \max Q(s',a') - Q(s,a)]$$

Here, $s$ = state, $a$ = action, $r$ = reward, $\alpha$ = learning rate, and $\gamma$ = discount factor. Over time, Q-learning converges to the optimal policy.

However, Q-learning struggles when the state-action space is very large, since storing all Q-values in a table becomes impractical.

This is where Deep Q-Networks (DQN) come in. A DQN combines Q-learning with deep neural networks to approximate the Q-function. Instead of maintaining a Q-table, a neural network predicts Q-values for all actions given a state. Techniques like experience replay (storing past experiences to reuse for training) and target networks (stabilizing updates) make training efficient and stable.

🔑 Key difference:

• Q-learning uses a lookup table for Q-values.

• DQN uses a neural network to approximate Q-values, making it scalable to complex environments like video games or robotics.

👉 In short, Q-learning is great for small problems, while DQN makes reinforcement learning practical for high-dimensional, real-world tasks.

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