Training action prediction machine-learning models for video games with healed data
US11786822B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 31, 2022 |
| Grant date | Oct 17, 2023 |
| Priority date | — |
| Expiry date | Mar 31, 2042 |
Classification
- Technology area (CPC A)Human Necessities
- CPC primaryA63F2300/6027
- WIPO fieldFurniture, games
- WIPO sectorOther fields
Abstract
This specification provides a computer-implemented method, the method comprising obtaining a machine-learning model. The machine-learning model is being trained with expert data comprising a plurality of training examples. Each training example comprises: (i) game state data representing a state of a video game environment, and (ii) scored action data representing an action and a score for that action if performed by a video game entity of the video game environment subsequent to the state of the video game environment. An action is performed by the video game entity based on a prediction for the action generated by the machine-learning model. The method further comprises determining whether the action performed by the video game entity was optimal. In response to determining that the action performed by the video game entity was suboptimal, a healed training example is generated. The healed training example comprises: (i) the state of the instance of the video game environment, and (ii) healed scored action data indicative that the action performed by the video game entity was suboptimal. The machine-learning model is updated based on the healed training example.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.