DQN
The Deep Q-Network (DQN) is a model-free, off-policy reinforcement learning algorithm that combines Q-learning with a deep neural network function approximator, learning to act directly from raw pixels.
Explore Google DeepMind through related topics and the articles other pages reference most.
Articles that also belong to these categories. Counts cover all of Google DeepMind.
Showing 1-5 of 5 articles
The Deep Q-Network (DQN) is a model-free, off-policy reinforcement learning algorithm that combines Q-learning with a deep neural network function approximator, learning to act directly from raw pixels.
GNoME (Graph Networks for Materials Exploration) is a deep-learning system from Google DeepMind that predicts the thermodynamic stability of inorganic crystals and uses those predictions to search for new…
Koray Kavukcuoglu is a Turkish computer scientist who leads Google DeepMind as its Senior Vice President, a role he assumed on 5 August 2026, and serves as Chief AI Architect of Google .
Veo is a family of text-to-video generative AI models developed by Google DeepMind, and is best known as the first video model from a leading AI lab to natively generate synchronized audio (dialogue, sound…
WaveNet is a deep generative model for raw audio waveforms developed by DeepMind that synthesizes speech by predicting one waveform sample at a time, each conditioned on all the samples before it.