[lukehack]

It's not monetizable, but I'm playing with reinforcement learning. It's incredible to watch a computer "learn" to play super mario using just input pictures.

Neural nets in general are much less complicated than I thought they would be, at least as a practitioner.

[Jugurtha]

Cool. How are you going about this ? Set up, stack, approches, experiments, using the model, etc. What's a typical playing session like ?

[lukehack]

The stack is basic. I develop on an old lenovo laptop and test for a few dozen frames(you can learn a lot without a CUDA GPU) before pushing it to a desktop with a cheap nvidia card. It uses pytorch and pyboy, and the model is just a couple Conv2d expansions and compressions before hitting a Linear layer outputting predicted reward for certain keypresses(basically). The model training is based off of deep Q learning. I'm looking at a pytorch tutorial[1] when I get stuck, but I'm trying to fumble around and try it myself as much as possible before looking at it.

I have an idea to have variable Q training propagation based on the amplitude of the reward so that bigger rewards propagate more, but I haven't got there yet.

Here is a great video on reinforcement learning[2].

[1] https://pytorch.org/tutorials/intermediate/mario_rl_tutorial...

[2] https://www.youtube.com/watch?v=93M1l_nrhpQ&t=3381