For hierarchical decomposition deep reinforcement learning for an artificial intelligence model
US11120365B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 14, 2018 |
| Grant date | Sep 14, 2021 |
| Priority date | — |
| Expiry date | Nov 21, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L67/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and apparatuses that apply a hierarchical-decomposition reinforcement learning technique to train one or more AI objects as concept nodes composed in a hierarchical graph incorporated into an AI model. The individual sub-tasks of a decomposed task may correspond to its own concept node in the hierarchical graph and are initially trained on how to complete their individual sub-task and then trained on how the all of the individual sub-tasks need to interact with each other in the complex task in order to deliver an end solution to the complex task. Next, during the training, using reward functions focused for solving each individual sub-task and then a separate one or more reward functions focused for solving the end solution of the complex task. In addition, where reasonably possible, conducting the training of the AI objects corresponding to the individual sub-tasks in the complex task, in parallel at the same time.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.