drubs

Outside of ML metrics, you're monitoring the health of every piece of hardware in the system. You need to make sure that you have every GPU, every CPU, the PCIe buses, the networking fabric are all working without any errors. You need to ensure that you can respond as fast as possible to any possible error. One bad component can bottleneck the entire job.

I remember being in the room with pretraining day 1 to help monitor the training job launch. Watching this model train from day 1 has been an amazing experience!

Fantastic work! This was a really fun collaboration.

Star the puffer https://github.com/PufferAI/PufferLib

Wouldn't make much sense. We generally train with 288 environments simultaneously. I've been thinking about ways to nicely stream all 288 environments though.

Really excited to be a part of the team!

Sounds cool to me.

Yup!

It's silly, but signs were a way to incentivize the agent to explore deeper into the Safari Zone among other areas.

My first version of this project 5 years ago involved a python-lua named pipe using Bizhawk actually. No clue where that code went

There's a ton of applications for AI. Back when I was at Spotify, I co-authored Basic Pitch (https://basicpitch.spotify.com/), an audio-to-midi library. There are a ton of uses for AI outside of what's heavily publicized.

There's an entire section on how the decompilations were used :)

Wrote about this in the results section. I think there is a way to mix the two and simplify the rewards in the process. A lot of the magic behind getting the agent to teach and use cut probably could have been handled by an LLM.

The environments wouldn't concentrate enough in the Rocket Hideout beneath Celadon Game Corner. The agent would have the player wander the world reward hacking. With wild battles enabled, the environments would end up in Lavender Tower fighting Gastly.

> (and how on earth did you port Pokémon red to a RL environment? O.o)

Read and find out :)

and...fixed!

Thanks for the heads up. I just pushed a fix.

Hi everyone!

After spending hundreds of hours, we're excited to finally share our progress in developing a reinforcement learning system to beat Pokémon Red. Our system successfully completes the game using a policy under 10M parameters, PPO, and a few novel techniques. With the release of Claude Plays Pokémon, now feels like the perfect time to showcase our work.

We'd love to get feedback and please join our Discord for more discussion (http://discord.gg/RvadteZk4G).

Enjoy!