Cooperative Modular Neural Networks for Artificial Intelligence in Games : A Comparison with A Monolithic Neural Network Regarding Technical Aspects and The Player Experience

Sammanfattning: Recent years have seen multiple machine-learning research projects concerning agents in video games. Yet, there is a disjoint between this academic research and the video game industry, evidenced by the fact that game developers still hesitate to use neural networks (NN) due to lack of clarity and control. Particularly for denizens, which are agents that take specific roles and have highly specialized purposes. Many denizens share features that could be exploited to reduce the hardship of training different types of denizens. A Cooperative Modular Neural Network (CMNN) seeks to provide more clarity and control than a monolithic neural network (Mono-NN) by breaking down the problem into specialist modules that exploit common denizen features and fuse them via a main network. The objective is to compare the CMNN and the Mono-NN in technical performance, and to compare the player satisfaction of playing against the two approaches in the same video game, Star Fetchers. The game was chosen because it belongs to the established genre of two-dimensional platforming games, providing a simple context. All NNs were implemented using the library TorchSharp. The approaches were compared on frame time, memory usage, and training time. A User Study of 58 participants' opinions regarding engagement and denizen movement was conducted and the results were analyzed for any statistical significance. The CMNN approach was shown to perform worse in frame time and memory usage. However, through parallelization of the modules, and by sharing modules between CMNNs, the gap can be bridged slightly. The training time was shown to be worse for the CMNN compared to the Mono-NN. Backward propagation, however, was faster for the CMNN, counterbalancing the time lost during forward propagation at shorter episode lengths. The CMNN also produces a minimum viable denizen in fewer epochs, significantly reducing the real-time spent training the denizen. The results of the User Study was inconclusive due to statistical insignificance. The CMNN is a viable competitor to Mono-NNs, at least in some aspects. Training is still costly in terms of time and effort and the complexity concerning hyperparameters and intelligent choice of reward function remains. However, the modules provide out-of-the-box networks that can be reused. More work within the area of cooperative modular methods is needed before the video game industry has any reason to make the change over from other time-proven methods.