Deep Q-Learning

Deep Q-Learning extends Q-Learning by using deep neural networks to approximate Q-values, enabling the algorithm to handle complex and high-dimensional state spaces more effectively. Experience replay is often incorporated to enhance stability and efficiency during learning.

Algorithm

See it in action here.

Pros

• High-Dimensional Inputs: Handles complex and high-dimensional state spaces, making it suitable for tasks involving raw sensory input like images.
• Feature Learning: Automatically learns relevant features from the input data through the use of deep neural networks.
• Generalization: Exhibits better generalization to unseen states compared to traditional tabular Q-Learning.
• Transfer Learning: Pre-trained networks can be used in different tasks, facilitating transfer learning.

Cons

• Computational Complexity: Deep Q-Learning can be computationally demanding, requiring significant resources for training, especially with large neural networks.
• Stability Challenges: Training deep networks for reinforcement learning can face challenges such as instability and divergence.
• Hyperparameter Sensitivity: Sensitivity to hyperparameters, making tuning and optimization more complex.
• Sample Efficiency: Requires a large number of samples to learn effectively, which can be impractical in certain situations.