Training actor-critic algorithms in laboratory settings
US12423571B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 26, 2020 |
| Grant date | Sep 23, 2025 |
| Priority date | — |
| Expiry date | Jan 23, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Reinforcement learning methods can use actor-critic networks where (1) additional laboratory-only state information is used to train a policy that much act without this additional laboratory-only information in a production setting; and (2) complex resource-demanding policies are distilled into a less-demanding policy that can be more easily run at production with limited computational resources. The production actor network can be optimized using a frozen version of a large critic network, previously trained with a large actor network. Aspects of these methods can leverage actor-critic methods in which the critic network models the action value function, as opposed to the state value function.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.