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Deep Reinforcement Learning: Pong from Pixels (2016) (karpathy.github.io)
111 points by Anon84 8 days ago | hide | past | favorite | 6 comments





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.


meh, u can still do that.

only difference is u have to explain the difference between your ML thing and the gen. AI hypetrain bullshit, every time.


>the gen. AI hypetrain bullshit

I seriously can't understand this take at all. Its like people going on and on about how much better horse drawn carriages are compared to cars.


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



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