DQN
Deep Q-Network
A **DQN**, or Deep Q-Network, approximates a state-value function in a [Q-Learning](https://paperswithcode.com/method/q-learning) framework with a neural network. In the Atari Games case, they take in several frames of the game as an input and output state values for each action as an output. 

It is usually used in conjunction with [Experience Replay](https://paperswithcode.com/method/experience-replay), for storing the episode steps in memory for off-policy learning, where samples are drawn from the replay memory at random. Additionally, the Q-Network is usually optimized towards a frozen target network that is periodically updated with the latest weights every $k$ steps (where $k$ is a hyperparameter). The latter makes training more stable by preventing short-term oscillations from a moving target. The former tackles autocorrelation that would occur from on-line learning, and having a replay memory makes the problem more like a supervised learning problem.

Image Source: [here](https://www.researchgate.net/publication/319643003_Autonomous_Quadrotor_Landing_using_Deep_Reinforcement_Learning)
Playing Atari with Deep Reinforcement Learning
https://paperswithcode.com/paper/playing-atari-with-deep-reinforcement
Playing Atari with Deep Reinforcement Learning