Advanced · Reinforcement Learning
DQN
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Deep Q-Network, a reinforcement learning algorithm combining deep learning with Q-learning for complex tasks.
Technical Definition
Deep Q-Network, a reinforcement learning algorithm combining deep learning with Q-learning for complex tasks.
How it works
Deep Q-Network (DQN) is an algorithm that enhances the traditional Q-learning method by using deep neural networks to approximate the Q-value function. This allows reinforcement learning agents to learn optimal policies in high-dimensional state spaces, such as those encountered in video games or complex control systems.
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.
- Deep Learning — A subset of machine learning using neural networks with many layers to learn hierarchical representations from large datasets.
- Q-Learning — A model-free RL algorithm that learns action values without knowing environment dynamics.