a dynamic balanced level generator for video games based on deep convolutional generative adversarial networks

In the gaming industry, creating well-balanced games is one of the major challenges developers are currently facing. balance in games has different meanings depending on the game type. but, most of the existing definitions are esteemed from the flow theory. flow theory in video games is stating that the level of challenge existing in the game must be neither too easy nor too difficult for the player. games that are not balanced will have a high churn rate and will suffer in terms of monetization. hence, nowadays a trending research area is focused on establishing mechanisms to create automatic balance in an algorithmic way. in this research, we have used generative adversarial networks (gans) to automatically create balanced levels. in the proposed work, a level of a 2d platformer game is fed to the network. finally, the network automatically generates new balanced levels and the levels are checked to see if they have the game’s minimum necessary requirements and also to check if they can be solved by the reinforcement learning agent. in the series of performed evaluations, it is shown that after the training process, the proposed approach is capable of generating levels that are well-balanced with considerable accuracy.