Deep Q-Networks (DQN) are a cornerstone of reinforcement learning (RL), combining Q-learning with deep neural networks to solve complex decision-making problems. This tutorial will guide you through the fundamentals of DQN and its implementation.

🧠 Core Concepts of DQN

  1. Q-Learning Basics

    • Q-values represent the expected future rewards for taking an action in a specific state.
    • The Bellman equation updates Q-values iteratively:
      $ Q(s, a) = r + \gamma \max_{a'} Q(s', a') $
      Q_Learning_Process

  2. Neural Network Integration

    • A deep neural network approximates the Q-value function, enabling scalability to high-dimensional inputs.
    • Input: Observations (e.g., game screen pixels); Output: Q-values for all possible actions.
      Deep_Q_Network_Structure

  3. Experience Replay

    • Stores past experiences in a buffer to break correlation between consecutive samples.
    • Improves training stability by sampling from a diverse set of transitions.
      Experience_Replay_Mechanism

🛠️ Implementation Steps

  1. Initialize the Q-Network

    • Use a convolutional neural network (CNN) for processing visual inputs.
    • Define a loss function (e.g., mean squared error) and optimizer (e.g., Adam).

  2. Train the Network with Target Network

    • Maintain a separate target network to compute target Q-values.
    • Update the target network periodically to ensure stability.

  3. Apply ε-Greedy Strategy

    • Balance exploration and exploitation by randomly selecting actions with probability ε.
    • Gradually reduce ε over time to favor optimal actions.

🎮 Application Case: Game Playing

DQN excels in environments like Atari games (e.g., Breakout, Pong).

  • The network learns to play by observing pixels and receiving rewards.
  • Example:
    Game_Theory_Application

For further exploration, check our Reinforcement Learning Introduction or Neural Network Basics Tutorial.