Selecting actions to be performed by a reinforcement learning agent using tree search
US10867242B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 29, 2016 |
| Grant date | Dec 15, 2020 |
| Priority date | — |
| Expiry date | Jun 21, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H50/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems and apparatus, including computer programs encoded on computer storage media, for training a value neural network that is configured to receive an observation characterizing a state of an environment being interacted with by an agent and to process the observation in accordance with parameters of the value neural network to generate a value score. One of the systems performs operations that include training a supervised learning policy neural network; initializing initial values of parameters of a reinforcement learning policy neural network having a same architecture as the supervised learning policy network to the trained values of the parameters of the supervised learning policy neural network; training the reinforcement learning policy neural network on second training data; and training the value neural network to generate a value score for the state of the environment that represents a predicted long-term reward resulting from the environment being in the state.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.