Stacked convolutional long short-term memory for model-free reinforcement learning
US10860927B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 27, 2019 |
| Grant date | Dec 8, 2020 |
| Priority date | — |
| Expiry date | Sep 27, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/092
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling an agent interacting with an environment. One of the methods includes obtaining a representation of an observation; processing the representation using a convolutional long short-term memory (LSTM) neural network comprising a plurality of convolutional LSTM neural network layers; processing an action selection input comprising the final LSTM hidden state output for the time step using an action selection neural network that is configured to receive the action selection input and to process the action selection input to generate an action selection output that defines an action to be performed by the agent at the time step; selecting, from the action selection output, the action to be performed by the agent at the time step in accordance with an action selection policy; and causing the agent to perform the selected action.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.