Robot Learning

Deep Q-Network (DQN)

Deep Q-Network (DQN) is a reinforcement learning algorithm that approximates the action-value function with a deep neural network, introduced by Mnih et al. and shown in 2015 to reach human-level play on Atari games from raw pixels. Its key stabilizing mechanisms, experience replay and a periodically updated target network, became standard components of value-based deep RL. DQN operates over discrete action sets.

Why it matters for physical AI

DQN demonstrated that control policies could be learned end-to-end from pixels, a founding result for learned visuomotor control. Its replay and target-network machinery persists in the off-policy algorithms used to train robot skills in simulation.