Control policies for collective robot learning
US11188821B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 15, 2017 |
| Grant date | Nov 30, 2021 |
| Priority date | — |
| Expiry date | Jul 1, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/39298
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, of training a global policy neural network. One of the methods includes initializing an instance of the robotic task for multiple local workers, generating a trajectory of state-action pairs by selecting actions to be performed by the robotic agent while performing the instance of the robotic task, optimizing a local policy controller on the trajectory, generating an optimized trajectory using the optimized local controller, and storing the optimized trajectory in a replay memory associated with the local worker. The method includes sampling, for multiple global workers, an optimized trajectory from one of one or more replay memories associated with the global worker, and training the replica of the global policy neural network maintained by the global worker on the sampled optimized trajectory to determine delta values for the parameters of the global policy neural network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.