Distributed training using actor-critic reinforcement learning with off-policy correction factors

US12299574B2 · kind B2 · utility

Assignee

• DeepMind Technologies Limited · GB

Inventors

• Hubert Josef Soyer · London, GB
• Lasse Espeholt · Amsterdam, NL
• Karen Simonyan · London, GB
• Yotam Doron · London, GB
• Vlad Firoiu · London, GB
• Volodymyr Mnih · Toronto, CA
• Koray Kavukcuoglu · London, GB
• Remi Munos · London, GB
• Thomas Ward · Boston, US
• Timothy James Alexander Harley · London, GB
• Iain Robert Dunning · New York, US

Key dates

• Filing date: Oct 16, 2023
• Grant date: May 13, 2025
• Expiry date: Oct 16, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/044
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.