State-augmented reinforcement learning
US11880765B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Oct 19, 2020 |
| Grant date | Jan 23, 2024 |
| Priority date | — |
| Expiry date | Apr 28, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/044
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A processor training a reinforcement learning model can include receiving a first dataset representing an observable state in reinforcement learning to train a machine to perform an action. The processor receives a second dataset. Using the second dataset, the processor trains a machine learning classifier to make a prediction about an entity related to the action. The processor extracts an embedding from the trained machine learning classifier, and augments the observable state with the embedding to create an augmented state. Based on the augmented state, the processor trains a reinforcement learning model to learn a policy for performing the action, the policy including a mapping from state space to action space.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.