Deep Reinforcement Learning: Pong from Pixels (2016)

I sometimes reminisce about the 2010-2020 deep learning and reinforcement learning etc era, as a student I did some projects in that domain back then and it felt very approachable and relatively easy to get into it compared to how I (most likely subjectively) see it today as a developer (in a different field).

I remember I doing a project in my second bachelor semester, where I generated random 64x64 images of a simple maze with a start and finish and then I tried to train a RNN algorithm that could navigate unseen mazes. There are so many better ways and algorithms to do it, but I learned a lot of cool tech anyway with this approach.

Here are some contemporary links for those looking to learn more about reinforcement learning:

- Reinforcement Learning Discussion on Discord, https://discord.com/invite/5nDB9dzZvp

- /r/reinforcementlearning, https://www.reddit.com/r/reinforcementlearning/

- CS285, Deep Reinforcement Learning course at Berkeley, https://rail.eecs.berkeley.edu/deeprlcourse/

And this was probably posted here before, but it's fun nonetheless: Gran Turismo played by an RL agent, https://www.gran-turismo.com/us/gran-turismo-sophy/

Edit: and there's this "blog" that gives some very practical advice for beginners: https://www.decisionsanddragons.com/

Compare also GAN Theft Auto: https://github.com/Sentdex/GANTheftAuto

Andrej just posted a video yesterday about LLMs and commented on the YC post for it: https://news.ycombinator.com/item?id=42952960