By running millions of simulations using trained agents to collect data, this ML-based game testing approach enables game designers to more efficiently make a game more fun, balanced, and aligned with their original vision. Today, we present an approach that leverages machine learning (ML) to adjust game balance by training models to serve as play-testers, and demonstrate this approach on the digital card game prototype Chimera, which we’ve previously shown as a testbed for ML-generated art. When games often have many different roles that can be played, with dozens of interconnecting skills, it makes it all the more difficult to hit the right balance. This process is not only time-consuming but also imperfect - the more complex the game, the easier it is for subtle flaws to slip through the cracks.
![game balance game game balance game](https://sc04.alicdn.com/kf/HTB1n9wjXE_rK1Rjy0Fcq6zEvVXaT.jpg)
![game balance game game balance game](https://4.bp.blogspot.com/-8QBxEsONUWQ/Vwg7R3KP_KI/AAAAAAAA5tY/t0fMnB4iW4graz-ynL65VEWC6AdhstAdA/s1600/Warwick_Splash_0.jpg)
![game balance game game balance game](https://www.boutiquesdemusees.fr/uploads/photos/21669/51688_xl.jpg)
#Game balance game software
Posted by Ji Hun Kim and Richard Wu, Software Engineers, Stadia