Tom Schaul
15Patents
3h-index
29Co-inventors
52Inventor score
Filing activity: Apr 17, 2015 → Mar 8, 2023
Most-cited inventions
| Patent | Title | Area | Cited by | Status |
|---|---|---|---|---|
| US10628733B2 | Selecting reinforcement learning actions using goals and observations | Physics | 14 | Active |
| US10282662B2 | Training neural networks using a prioritized experience memory | Emerging Cross-Sectional Technologies | 8 | Active |
| US10733501B2 | Environment prediction using reinforcement learning | Physics | 3 | Active |
| US10650310B2 | Training neural networks using a prioritized experience memory | Emerging Cross-Sectional Technologies | 3 | Active |
| US10956820B2 | Reinforcement learning with auxiliary tasks | Physics | 2 | Active |
| US12154029B2 | Continual reinforcement learning with a multi-task agent | Physics | 1 | Active |
| US12061964B2 | Modulating agent behavior to optimize learning progress | Physics | 1 | Active |
| US12141677B2 | Environment prediction using reinforcement learning | Physics | 1 | Active |
| US12086714B2 | Training neural networks using a prioritized experience memory | Emerging Cross-Sectional Technologies | 0 | Active |
| US11615310B2 | Training machine learning models by determining update rules using recurrent neural networks | Physics | 0 | Active |
| US10055687B2 | Method for creating predictive knowledge structures from experience in an artificial agent | Physics | 0 | Active |
| US11568250B2 | Training neural networks using a prioritized experience memory | Emerging Cross-Sectional Technologies | 0 | Active |
| US11842281B2 | Reinforcement learning with auxiliary tasks | Physics | 0 | Active |
| US11676035B2 | Learning non-differentiable weights of neural networks using evolutionary strategies | Physics | 0 | Active |
| US12271823B2 | Training machine learning models by determining update rules using neural networks | Physics | 0 | Active |
Source: USPTO / EPO open patent data. Inventor disambiguation is heuristic; counts are objective bibliographic measures.