Abstract: Systems and methods for enhanced training of machine learning systems based on automatically generated visually realistic gameplay. An example method includes obtaining electronic game data that includes rendered images and associated annotation information, the annotation information identifying features included in the rendered images to be learned, and the electronic game data being generated by a video game associated with a particular sport. Machine learning models are trained based on the obtained electronic game data, with training including causing the machine learning models to output annotation information based on associated input of a rendered image. Real-world gameplay data is obtained, with the real-world gameplay data being images of real-world gameplay of the particular sport. The obtained real-world gameplay data is analyzed based on the trained machine learning models. Analyzing includes extracting features from the real-world gameplay data using the machine learning models.

Abstract: System and methods for utilizing a video game console to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and collect game state data corresponding to how the player reacts to the particular gameplay situation or an effect of the reaction. In some cases, the video game console can receive an exploratory rule set to apply during the particular gameplay situation. In some cases, the video game console can trigger the particular gameplay situation. A system can receive the game state data from many video game consoles and train a rule set based on the game state data. Advantageously, the system can save computational resources by utilizing the players' video game experience to train the rule set.

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.

Abstract: Systems and methods for enhanced training of machine learning systems based on automatically generated visually realistic gameplay. An example method includes obtaining electronic game data that includes rendered images and associated annotation information, the annotation information identifying features included in the rendered images to be learned, and the electronic game data being generated by a video game associated with a particular sport. Machine learning models are trained based on the obtained electronic game data, with training including causing the machine learning models to output annotation information based on associated input of a rendered image. Real-world gameplay data is obtained, with the real-world gameplay data being images of real-world gameplay of the particular sport. The obtained real-world gameplay data is analyzed based on the trained machine learning models. Analyzing includes extracting features from the real-world gameplay data using the machine learning models.

Abstract: System and methods for utilizing a video game console to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and collect game state data corresponding to how the player reacts to the particular gameplay situation or an effect of the reaction. In some cases, the video game console can receive an exploratory rule set to apply during the particular gameplay situation. In some cases, the video game console can trigger the particular gameplay situation. A system can receive the game state data from many video game consoles and train a rule set based on the game state data. Advantageously, the system can save computational resources by utilizing the players' video game experience to train the rule set.

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.

Abstract: An artificially intelligent entity can emulate human behavior in video games. An AI model can be made by receiving gameplay logs of a video gameplay session, generating, based on the gameplay data, first situational data indicating first states of the video game, generating first control inputs provided by a human, the first control inputs corresponding to the first states of the video game, training a first machine learning system using the first situational data and corresponding first control inputs, and generating, using the first machine learning system, a first artificial intelligence model. The machine learning system can include a convolutional neural network. Inputs to the machine learning system can include a retina image and/or a matrix image.

Abstract: Systems and methods for enhanced training of machine learning systems based on automatically generated visually realistic gameplay. An example method includes obtaining electronic game data that includes rendered images and associated annotation information, the annotation information identifying features included in the rendered images to be learned, and the electronic game data being generated by a video game associated with a particular sport. Machine learning models are trained based on the obtained electronic game data, with training including causing the machine learning models to output annotation information based on associated input of a rendered image. Real-world gameplay data is obtained, with the real-world gameplay data being images of real-world gameplay of the particular sport. The obtained real-world gameplay data is analyzed based on the trained machine learning models. Analyzing includes extracting features from the real-world gameplay data using the machine learning models.

Abstract: An artificially intelligent entity can emulate human behavior in video games. An AI model can be made by receiving gameplay logs of a video gameplay session, generating, based on the gameplay data, first situational data indicating first states of the video game, generating first control inputs provided by a human, the first control inputs corresponding to the first states of the video game, training a first machine learning system using the first situational data and corresponding first control inputs, and generating, using the first machine learning system, a first artificial intelligence model. The machine learning system can include a convolutional neural network. Inputs to the machine learning system can include a retina image and/or a matrix image.