EFFICIENT GAMEPLAY TRAINING FOR ARTIFICIAL INTELLIGENCE

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 m...

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Bibliographische Detailangaben
Hauptverfasser:	Martz, Nathan Sun, Moraldo, Horacio Hernan, Miles, Stewart, Haller, Leopold, Sakazaki, Hinako
Format:	Patent
Sprache:	eng
Schlagworte:	AMUSEMENTS CALCULATING CARD, BOARD, OR ROULETTE GAMES COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING GAMES GAMES NOT OTHERWISE PROVIDED FOR HUMAN NECESSITIES INDOOR GAMES USING SMALL MOVING PLAYING BODIES PHYSICS SPORTS VIDEO GAMES
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Beschreibung
Zusammenfassung:	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.