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.

This paper assesses an improved framework for evaluating the performance of economies within online multiplayer games. Games in this medium traditionally require a great deal of testing and analysis to assess the outcomes and affects of player interactions. This process is normally imperfect and time consuming, leading a lot of games developers and publishers to maintain a lot of risk during the development and launch of multi-million dollar projects. The framework and example project presented within this paper uses deep reinforcement learning to simulate economic interactions between learning agents. This is possible by having agents learn from player demonstrations to interact with game systems such as Battling, Collecting Resources and Crafting Weapons which allows them to co-ordinate and cooperate to achieve long-term goals in the game. This paper shows recent breakthroughs in relation to training these agents to the variety of metrics learning agents can generate to help games designers test and polish multiplayer experience, which have proved hard to quantify and measure without extensive play testing. This paper is an extension paper to our publication in ICAART 2021 [21], within this paper includes the following additions:

This paper proposes a new framework to measure the fairness of asymmetric level-design in multiplayer games. This work achieves real time prediction of the degree to which asymmetric levels are balanced using deep learning. The proposed framework provides both cost and time savings, by removing the requirement of numerous designed levels and the need to gather player data samples. This advancement with the field is possible through the combination of deep reinforcement learning (made accessible to developers with Unity’s ML-Agents framework), and Procedural Content Generation (PCG). The result of this merger is the acquisition of accelerated training data, which is established using parallel simulations. This paper showcases the proposed approach on a simple two player top-down -shooter game implemented using MoreMountains: Top Down Engine an extension to Unity 3D a popular game engine. Levels are generated using the same PCG approaches found in ’Nuclear Throne’ a popular cross platform Roguelike published by Vlambeer. This approach is accessible and easy to implement allowing games developers to test human-designed content in real time using the predictions. This research is open source and available on Github: https://github.com/Taikatou/top-down-shooter.

Balanced intransitive relationships are critical to the depth of strategy and player retention within esports games. Intransitiverelationships comprise the metagame, a collection of strategies and play styles that are viable, each providing