https://www.youtube.com/watch?v=yRV8fSw6HaE
But there's more to the setup than you might assume from a casual reading. Here's the code used for that demo:
https://github.com/SeanCole02/doom-neuron
So there is an entire pytorch stack wrapped around the mysterious little blob of neurons -- they aren't just wired straight into WASD. There is a conventional convnet-based encoder, running on a GPU, in the critical path. The README tries to argue that the "neurons are doing the learning" but to my dilettante, critical eye it really looks as though there is a hell of a lot of learning happening in the convnet also.
Are the neurons learning to play doom, or are they learning to inject ever so slightly more effective noise into the critical path? Would this work just as well if we replaced the neurons with some other non-markovian sludge? The authors do ablation experiments to try to get to the bottom of this but I can't really tell how compelling the results are (due to my own ignorance/stupidity of course)