Deep Q-Network (DQN) Tutorial with TensorFlow

A hands-on guide to implementing Deep Q-Networks using TensorFlow for reinforcement learning tasks.

What is DQN?

Deep Q-Network (DQN) is a reinforcement learning algorithm that combines Q-learning with deep neural networks to approximate the action-value function. It's widely used in environments like games, robotics, and more.

✅ Key Concepts:

• Q-value: Estimated reward of taking an action in a specific state
• Neural Network: Learns to predict Q-values from raw observations
• Experience Replay: Stores past experiences to improve learning stability
• Target Network: Helps reduce correlation between training samples

Implementation Steps

1. Install Dependencies

   pip install tensorflow gym
   

   📌 Check TensorFlow installation guide

2. Build the DQN Model

   model = tf.keras.Sequential([
       tf.keras.layers.Dense(24, activation='relu', input_shape=(state_size,)),
       tf.keras.layers.Dense(24, activation='relu'),
       tf.keras.layers.Dense(action_size)
   ])
   

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3. Train the Model
   Use the CartPole environment as a classic example:
   📌 Explore CartPole environment details
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4. Evaluate Results
   Monitor training progress with metrics like reward and loss.

Further Learning

📖 TensorFlow Reinforcement Learning Guide
🤖 Watch a video tutorial on DQN