Balancing Multiplayer Games across Player Skill Levels using Deep Reinforcement Learning

The balance or perceived fairness of Level & Character design within multiplayer games depends on the skill level of the players within the game, skills or abilities that have high contributions but require low skill, feel unfair for less skill players and can become the dominant strategy and playstyle if left unchecked. Player skill influences the viable tactics for different map designs, with some strategies only possible for the best players. Level designers hope to create various maps within the game world that are suited to different strategies, giving players interesting choices when deciding what to do next. This paper proposes using deep learning to measure the connection between player skills and balanced level design. This tool can be added to Unity game engine allowing designers to see the impact of their changes on the level’s design on win-rate probability for different skilled teams. The tool is comprised of a neural network which takes as input the level layout as a stacked 2D one hot encoded array alongside the player parameters, skill rating chosen characters; the neural network output is the win rate probability between 0-1 for team 1. Data for this neural network is generated using learning agents that are learning the game using self-play (Silver et al., 2017) and the level data that is used for training the neural network is generated using procedural content generation (PCG) techniques.