Recently, Google AI has announced that the use of a machine learning system for rapid training to game-playing agents. This framework can be used by game developers to release human game-playing agents, who can efficiently focus on other priority duties to boost productivity. Google AI will provide an open-source library to show the techniques used in this practice. Google AI wants to harness machine learning algorithms for designers to balance their games, artists to generate assets of top-notch quality within a short period of time. The models can also be used to train challenging opponents to compete at the highest level of any game efficiently.

Traditionally, game developers leverage machine learning algorithms for direct access to the source, the uniquely interactive nature of video games, and many more functionalities. But, in the current scenario, Google AI has launched a machine learning system that can be used for rapid training to game-playing agents and seeking serious bugs instantly. The modern solution can work on some popular game genres and generate game actions from a game state within one hour on a single game. Google AI is determined to produce a machine learning system that can only play the game for the game developers while detecting and fixing bugs automatically.

This updated machine learning system can allow game developers to train multiple game-playing agents instead of one super-effective agent with a single end-to-end machine learning model. Game developers experienced a most fundamental barrier in implementing machine learning to computer games- bridging the gap between the simulation-centric world of games and the data-centric world of machine learning. The current machine learning system provides efficient as well as game-developer-friendly APIs with a Dagger-inspired interactive training flow to develop user-friendly video games by describing what a player perceives and the semantic actions related to it like joysticks, 3D objects, 3D locations, buttons, and many more.

Google AI is ready to provide an open-source library for game developers with no prior knowledge of machine learning while the training of game-playing agents can be finished within an hour on a single developer machine. The reason is the effective performance of imitation learning that teaches machine learning models by observing the behaviors of professional players in the games. The imitation model is inspired by the DAgger algorithm that allows taking an advantage of the interactivity of video games.