Advanced · Reinforcement Learning
Deep Q-Network (DQN)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A type of Q-learning algorithm that uses a deep neural network to approximate the Q-value function.
Technical Definition
A type of Q-learning algorithm that uses a deep neural network to approximate the Q-value function.
How it works
A Deep Q-Network (DQN) combines Q-learning with deep neural networks to handle high-dimensional state spaces, like those found in image-based reinforcement learning tasks. The neural network learns to predict the optimal action-value function (Q-function) for each state. This allows agents to learn effective policies in complex environments where traditional Q-learning would be intractable.
Related Concepts
- Neural Network — A computing system inspired by biological neural networks that learns patterns from data through interconnected layers of nodes.
- Reinforcement Learning — A paradigm where an agent learns to make decisions by receiving rewards or penalties from its environment through trial and error.
- Q-Learning — A model-free RL algorithm that learns action values without knowing environment dynamics.