Inverse reinforcement learning for user-specific behaviors
US11710072B1 · kind B1 · utility
Inventors
Key dates
| Filing date | Jun 28, 2021 |
| Grant date | Jul 25, 2023 |
| Priority date | — |
| Expiry date | Jun 28, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2200/24
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In one implementation, a method for inverse reinforcement learning for tailoring virtual agent behaviors to a specific user. The method includes: obtaining an initial behavior model for a virtual agent and an initial state for a virtual environment associated with the virtual agent, wherein the initial behavior model includes one or more tunable parameters; generating, based on the initial behavior model and the initial state for the virtual environment, a first set of behavioral trajectories for the virtual agent; obtaining a second set of behavioral trajectories from a source different from the initial behavior model; and generating an updated behavior model by adjusting at least one of the one or more tunable parameters of the initial behavior model as a function of the first and second sets of behavioral trajectories, wherein at least one of the first and second sets of behavioral trajectories are assigned different weights.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.