What is deep Q-learning?

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Deep Q-Learning (DQL) is an extension of Q-learning that uses deep neural networks to approximate the Q-values instead of a Q-table. This allows reinforcement learning to work in environments with large or continuous state spaces, where traditional Q-learning’s table-based approach becomes infeasible.

Key Concepts of Deep Q-Learning

1. Q-Function Approximation

   • Instead of storing Q-values in a table (Q(s, a)), a deep neural network (DQN) is used to predict the Q-value for any state-action pair.

   • Input: current state s

   • Output: Q-values for all possible actions in that state

2. Experience Replay

   • To stabilize learning, past experiences (state, action, reward, next_state) are stored in a replay buffer.

   • The agent samples random batches from this buffer to train the neural network, reducing correlation between consecutive experiences.

3. Target Network

   • Deep Q-Learning uses a target network, a copy of the main Q-network, to calculate stable target Q-values.

   • The target network is updated periodically, preventing oscillations or divergence during training.

4. Learning Objective

   • The network is trained to minimize the difference between predicted Q-values and target Q-values derived from the Bellman equation:

     Target = r + γ * max Q_target(s', all actions)
     Loss = (Q(s, a) - Target)^2
     

     • γ = discount factor

     • r = immediate reward

     • s' = next state

Example Scenario (Conceptual)

• Environment: Atari game like “Breakout” with high-dimensional pixel inputs.

• Problem with classical Q-learning: Too many states (every pixel combination) to store in a table.

• Solution: Use a convolutional neural network (CNN) to approximate Q-values for each possible action, allowing the agent to learn optimal strategies directly from raw screen images.

✅ Summary

Deep Q-Learning combines Q-learning with deep neural networks to handle complex, high-dimensional environments. It uses techniques like experience replay and target networks to stabilize learning, enabling agents to learn policies directly from raw inputs such as images or sensor data.

Read more:

What is reinforcement learning?

What is Q-learning?

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