Simple Guide to Deep Q-Networks (DQNs)

The Deep Q-Network (DQN) is an innovative reinforcement learning (RL) algorithm that combines both deep learning and Q-Learning.

Introduced by DeepMind in 2015, the DQN shows how deep neural networks can be useful in many domains, even in complex RL tasks. DQNs can take in high-dimensional sensory inputs, beating traditional RL methods, which had difficulty scaling to large state spaces.

As a quick brush-up, reinforcement learning is a subgroup of machine learning that allows an agent to learn in an environment by trial and error using feedback from its actions. The big idea is to allow computers to learn from their mistakes.

Q-Learning is one of the fundamental algorithms of reinforcement learning, being closely related to the computer science concept of dynamic programming, often using a 2D array (which we call a Q-table) to store values.