Adversarial agent controls generation and problematic scenario forecasting
US11891088B1 · kind B1 · utility
3Cited by
0References
20Claims
0Family size
Assignee
Inventors
Key dates
| Filing date | Jun 14, 2021 |
| Grant date | Feb 6, 2024 |
| Priority date | — |
| Expiry date | Apr 18, 2042 |
Classification
- Technology area (CPC B)Performing Operations; Transporting
- CPC primaryB60W2556/45
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
A reward determined as part of a machine learning technique, such as reinforcement learning, may be used to control an adversarial agent in a simulation such that a component for controlling motion of the adversarial agent is trained to reduce the reward. Training the adversarial agent component may be subject to one or more constraints and/or may be balanced against one or more additional goals. Additionally or alternatively, the reward may be used to alter scenario data so that the scenario data reduces the reward, allowing the discovery of difficult scenarios and/or prospective events.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.