Deep learning in multiplayer online battle arena games

Abstract

Multiplayer Online Battle Arena games, known as MOBA in abbreviation, are developing rapidly, and more and more new players are growing interests to it. But some parts of these games are quite complicate for those beginners, such as how to pick appropriate champions, how to choose suitable items for purchasing, what is the win rate for current game session and how to make correct strategy decisions. This paper summarized some works, that can help players to solve those complicate parts and understand the game well, using machine learning and deep learning models. These works have all proved their feasibility according to either their result comparing with other baseline methods, or simulating some game sessions played by or against AI using their champion picking, item purchasing and strategy making suggestions. There are also some limitations of these works and some improvements of using machine learning and deep learning in MOBA game industry mentioned in this paper.