Control policies for robotic agents
US10960539B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 15, 2017 |
| Grant date | Mar 30, 2021 |
| Priority date | — |
| Expiry date | Oct 27, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/39164
- 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 a plurality of instances of the robotic task. For each instance of the robotic task, the method includes 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 in accordance with current values of the parameters of the global policy neural network, and optimizing a local policy controller that is specific to the instance on the trajectory of state-action pairs for the instance. The method further includes generating training data for the global policy neural network using the local policy controllers, and training the global policy neural network on the training data to adjust the current values of the parameters of the global policy neural network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.