Abstract: Systems and methods for optimizing a Lifetime Value (LTV) of a player of a plurality of computer-implemented games are disclosed. Data is collected from a game of the plurality of games, the data including game event data associated with the player, a playing environment within the game, and store action data. 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 including a functional relationship proposing a selection of one or more store actions within a store to maximize the LTV. One or more of the store actions chosen from the proposed selection in accordance with the ML policy and implemented within the store environment.

Abstract: 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.