Globally Optimized Matchmaking in Online Games

Abstract

As one of the core components of online games, matchmaking is the process of arranging multiple players into matches, where the quality of matchmaking systems directly determines player satisfaction and further affects the life cycle of game products. With the number of candidate players increases, the number of possible match combinations grows exponentially, which makes the current implementation for multiplayer matchmaking can only obtain locally optimal arrangement in an inefficient fashion. In this paper, we focus on the globally optimized matchmaking problem, in which the objective is to decide an optimal matching sequence for the queuing players. To tackle this challenging problem, we propose a novel data-driven matchmaking framework, called GloMatch, based on machine learning principles. Through transforming the matchmaking problem into a sequential decision problem, we solve it with the help of an effective policy-based deep reinforcement learning algorithm. Quantitative experiments on simulation and online game environments demonstrate the effectiveness of the presented framework.