Balancing intransitive relationships in moba games using deep reinforcement learning

Balanced intransitive relationships are critical to the depth of strategy and player retention within esports games. Intransitive relationships comprise the metagame, a collection of strategies and play styles that are viable, each providing counterplay for other viable strategies. This work presents a framework for testing the balance of massive online battle arena (MOBA) games using deep reinforcement learning to identify the synergies between characters by measuring their effectiveness against the other compositions within the games character roster. This research is designed for game designers and developers to show how multi-agent reinforcement learning (MARL) can accelerate the balancing process and highlight potential game-balance issues during the development process. Our findings conclude that accurate measurements of game balance can be found with under 10 hours of simulation and show imbalances that traditional cost curve analysis approaches failed to capture. Furthermore, we discovered that this approach reduced imbalance in each character's win rate by 20% in our example project a key measurement that would be impossible to measure without collecting data from hundreds of human-controlled games previously. The project's source code is publicly available at https://github.com/Taikatou/topdown- shooter.