A method for optimizing LTV related to cumulative future rewards for a player of a plurality of computer-implemented games is disclosed. Data is collected from a game of the plurality of games. The data includes game event data associated with the player, a playing environment within the game, and engine actions performable by the LTV module. The data is analyzed with a first machine-learning (ML) system to create a time-dependent state representation of the game, the player, and the playing environment. The state representation is provided as input to a second ML system to create and optimize an ML policy over time. The ML policy includes a functional relationship proposing a selection of one or more of the engine actions to maximize the LTV. The ML policy and state representation is provided to an LTV optimization module to choose and implement one or more of the engine actions.
METHODS AND SYSTEMS FOR USING A GAMING ENGINE TO OPTIMIZE LIFETIME VALUE OF GAME PLAYERS WITH ADVERTISING AND IN-APP PURCHASING