Deep Q-Networks (DQN): Combining Neural Networks with Reinforcement Learning
artificial-intelligence-ai.
Course Modules:
Module 1: Why Deep Q-Networks?
Limitations of Q-tables in complex environments
Role of neural networks in approximating Q-values
Success stories: Atari games, robotics, and beyond
Module 2: DQN Architecture Explained
State → Neural Network → Q-values
Structure of the input layer, hidden layers, and output layer
How the network predicts the best action for each state
Module 3: Core DQN Algorithm
Experience replay: sampling past experiences for learning
Target network: stabilizing Q-value updates
Training loop with Bellman loss function and gradient descent
Module 4: Implementing a DQN Agent
Using OpenAI Gym for environment setup (e.g., CartPole, LunarLander)
Building the neural network in TensorFlow or PyTorch
Training, evaluating, and improving the agent
Module 5: Tuning and Enhancing DQN
Choosing learning rate, epsilon decay, replay buffer size
Double DQN and Dueling DQN enhancements
Monitoring performance with visual plots and logs
Module 6: Capstone Project – Build a Deep Q-Agent
Select an environment (Atari game or Gym environment)
Train and test a DQN agent
Submit a project report with model architecture, training results, and lessons learned
Tools & Technologies Used:
Python
TensorFlow or PyTorch
OpenAI Gym
Matplotlib, NumPy, Seaborn for visualization
Target Audience:
AI developers and ML engineers
Intermediate learners in reinforcement learning
Students interested in building intelligent agents
Professionals applying AI to simulation, gaming, or robotics
Global Learning Benefits:
Understand how deep learning enhances reinforcement learning
Build scalable agents that learn from complex environments
Apply DQN to real-world problems and research challenges
Gain confidence in building custom RL pipelines using neural networks
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