A Deep Q-Network (DQN) is an advanced reinforcement learning model that combines Q-Learning with deep neural networks to handle complex, high-dimensional environments.
Unlike standard Q-Learning, which uses a Q-table, DQNs approximate Q-values using neural networks, making them suitable for tasks like video games and robotics.
How DQN Works
- Input State: Feed the current environment state into a neural network.
- Predict Q-Values: The network outputs Q-values for all possible actions.
- Choose Action: Select action using ε-greedy or other strategies.
- Perform Action: Execute in the environment and receive reward.
- Update Network: Adjust weights using loss based on the Q-Learning formula.
- Replay Memory: Store past experiences and train in batches to stabilize learning.
Advantages of DQN
- Handles large and continuous state spaces
- Learns complex policies in high-dimensional environments
- Combines reinforcement learning with deep learning
- Can solve challenging tasks like Atari games and robotics
Disadvantages
- Requires substantial computational resources
- Training can be slow and unstable without tuning
- Sensitive to hyperparameters like learning rate and batch size
Real-World Examples
- Playing video games like Atari at superhuman level
- Autonomous driving simulation
- Robot control and navigation
- Trading strategies in finance
- Dynamic resource management
Conclusion
DQN extends Q-Learning with deep learning, enabling AI to tackle high-dimensional problems and complex decision-making environments efficiently.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/