Well, a little shark, anyway. There’s an excellent course on RL produced by DeepLizard. All the videos are available on YT (for free of course) but they do have a paid course with some extras, although I’m not sure those extras are worth it (small community involvement, etc). However I don’t mind paying content creators, especially for good stuff.
Towards the end of the course we get to the CartPole environment in OpenAI Gym. Now this environment produces state as a set of 4 values related to position and movement of the cart and the pole, but for some reason the course presenter has decided to use image data from renders as the state. She has mentioned quite a bit that this is how AI learned to play Atari games originally, but the code required for image processing seems to be a little overkill to me, especially with a perfectly adequate alternative provided by default.
Well, is it adequate? I guess I’ll try to implement the solution using the provided state instead of her modification, and see what kind of results I get. At least the whole thing is in PyTorch so I won’t have to worry about conversions from TF (which I haven’t totally got sorted yet, unfortunately)
ETA: Actually, she did rework the solution to use the state data provided by the gym environment rather than using image data from renders, with better results. I guess a willingness to explore options is a good thing, especially in the realm of machine learning!