- 专利标题: EFFICIENT GAMEPLAY TRAINING FOR ARTIFICIAL INTELLIGENCE
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申请号: US18554931申请日: 2022-04-11
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公开(公告)号: US20240198232A1公开(公告)日: 2024-06-20
- 发明人: Nathan Sun Martz , Horacio Hernan Moraldo , Stewart Miles , Leopold Haller , Hinako Sakazaki
- 申请人: GOOGLE LLC
- 申请人地址: US CA Mountain View
- 专利权人: GOOGLE LLC
- 当前专利权人: GOOGLE LLC
- 当前专利权人地址: US CA Mountain View
- 国际申请: PCT/US2022/024192 2022.04.11
- 进入国家日期: 2023-10-11
- 主分类号: A63F13/67
- IPC分类号: A63F13/67 ; A63F13/352 ; A63F13/79 ; G06N5/04
摘要:
Systems and methods are described for training a locally executed actor component to execute real-time gameplay actions in a gaming application based on one or more gameplay data models generated by a remote learning service. A gameplay data model for the gaming application is provided from one or more server computing systems executing the remote learning service to the client computing device. Observational data is generated by the local actor component based on in-game results of artificial gameplay actions performed by the local actor component, based at least in part on inferences generated by the actor component using the provided gameplay data model. Based on the received observational data, the remote learning service modifies the gameplay data model and provides the modified gameplay data model to the local actor component to improve future artificial gameplay actions.
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