Automated reinforcement learning scenario variation and impact penalties
US12037013B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 29, 2021 |
| Grant date | Jul 16, 2024 |
| Priority date | — |
| Expiry date | Oct 6, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F11/3698
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Automating reinforcement learning for autonomous vehicles may include assigning a probability with a scenario and varying that probability based at least in part on changes in performance by the autonomous vehicle associated with that scenario. The amount of time and computational bandwidth required to train a machine-learned component of an autonomous vehicle and the accuracy of the machine-learned component may be improved by determining a reward for performance of the autonomous vehicle in a scenario based at least in part on an severity metric. The impact severity metric may be determined based at least in part on a velocity, angle, and/or interaction area associated with the impact.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.