Mario Kart 8 Deluxe may be fun to play on the Nintendo Switch, but this takes racing to the next level. Seth Bling is a programmer who has an affinity for Mario games as well as artificial intelligence. So, he decided to combine these two to create MariFlow, a neural network that has been designed to play Super Mario Kart. To train the AI, it was shown 15 hours of video of his father's play style, and at the end, it managed to to win gold in the 50cc Mushroom Cup. When the network got into tricky situations, Seth took over the controls to help teach the AI on how they should be tackled.

I really liked your previous project, and to be honest I enjoyed that more than the current project. In the previous project, your agents learned how to play a game from scratch by evolving a minimal neural network. Combining that approach with an algorithm to generate random tracks, or play against itself in the experiment, it may even learn to generalize to some extent to previously unseen tracks. Here, I see you are training a predictive coding model to imitate a recorded dataset of actual human play, which is better than Mar I/O from a technical standpoint in the sense that you are learning from pixels, but conceptually I am still more interested in the self-exploration idea. It might be cool though to train your LSTM to imitate the NEAT-evolved agents from Mar I/O, then you can claim the entire system learned to play on its own!