Gamer training using neural networks

Abstract

Personalized coaching is provided to users of an application, such as players of an electronic gaming application. Data can be obtained that demonstrates how skilled users utilize an application, such as how professional players play a game. This data can be used to train a machine learning model for the game. Gameplay data for an identified player can be obtained, and related information provided as input to the trained model. The model can infer one or more actions or strategies to be taken by the player in order to achieve a determined goal. The information can be conveyed to the player using visual, audio, or haptic guidance during gameplay, or can be provided offline, such as with video or rendered replay of the game session. The types of advice or coaching given can vary depending upon factors such as the goals, skill level, and preferences of the player, and can update over time.