Invention Grant
- Patent Title: Customized models for imitating player gameplay in a video game
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Application No.: US16460871Application Date: 2019-07-02
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Publication No.: US10940393B2Publication Date: 2021-03-09
- Inventor: Caedmon Somers , Jason Rupert , Igor Borovikov , Ahmad Beirami , Yunqi Zhao , Mohsen Sardari , Harold Henry Chaput , Navid Aghdaie , Kazi Atif-Uz Zaman
- Applicant: Electronic Arts Inc.
- Applicant Address: US CA Redwood City
- Assignee: Electronic Arts Inc.
- Current Assignee: Electronic Arts Inc.
- Current Assignee Address: US CA Redwood City
- Agency: Knobbe, Martens, Olson & Bear, LLP
- Main IPC: A63F13/67
- IPC: A63F13/67 ; A63F13/56 ; G06N20/00

Abstract:
Systems and methods are disclosed for training a machine learning model to control an in-game character or other entity in a video game in a manner that aims to imitate how a particular player would control the character or entity. A generic behavior model that is trained without respect to the particular player may be obtained and then customized based on observed gameplay of the particular player. The customization training process may include freezing at least a subset of layers or levels in the generic model, then generating one or more additional layers or levels that are trained using gameplay data for the particular player.
Public/Granted literature
- US20210001229A1 CUSTOMIZED MODELS FOR IMITATING PLAYER GAMEPLAY IN A VIDEO GAME Public/Granted day:2021-01-07
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