Q-Learning Networks

Deep Q-Network

Introduced by Mnih et al. in Playing Atari with Deep Reinforcement Learning

A DQN, or Deep Q-Network, approximates a state-value function in a 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, 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

Source: Playing Atari with Deep Reinforcement Learning


Paper Code Results Date Stars


Task Papers Share
Reinforcement Learning (RL) 277 39.24%
Atari Games 65 9.21%
Decision Making 32 4.53%
Management 17 2.41%
Multi-agent Reinforcement Learning 14 1.98%
Efficient Exploration 14 1.98%
OpenAI Gym 12 1.70%
Autonomous Driving 11 1.56%
Continuous Control 9 1.27%