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Re:MuZero: Mastering Go, chess, shogi and Atari without rules by syntaxing on Wednesday December 23, 2020 @06:02PM (#25523308)
As impractical as the idea is, reinforcement learning is so damn fun. I highly recommend others to play around with it. I originally was using the famous fork of OpenAi baseline, stable baseline but had issues with tuning with Optuna. I recently stumbled across Ray from Berkley [1] and it has a newer and fancier built-in hyper-parameter tuner. Even as a hardware engineer that's only a software hobbyist can make the computer play some atari games. I think my next step is to try to make my own Super Mario agent.

[1] https://docs.ray.io/en/latest/index.html

Re:MuZero: Mastering Go, chess, shogi and Atari without rules by nightcracker on Wednesday December 23, 2020 @07:01PM (#25523736)
Eh, I'd say it's fun if you have a couple thousand TPUs lying around.

If you're just messing around with 1 GPU and a desktop PC you should be happy to get Atari breakout to work.

Re:MuZero: Mastering Go, chess, shogi and Atari without rules by syntaxing on Wednesday December 23, 2020 @07:17PM (#25523848)
Definitely the worse part of RL is that it takes so long to train. But it works surprisingly well on Google Colab or equivalent.
Re:MuZero: Mastering Go, chess, shogi and Atari without rules by confuseshrink on Wednesday December 23, 2020 @09:14PM (#25524475)
The published hyperparameters are usually ridiculously conservative, for the simple games like breakout and pong you can usually converge in far fewer frames than in the papers.
Re:MuZero: Mastering Go, chess, shogi and Atari without rules by nightcracker on Thursday December 24, 2020 @09:21AM (#25527657)
I have tried reproducing the papers, with mixed success. I do not share your sentiment.