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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



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.


Definitely the worse part of RL is that it takes so long to train. But it works surprisingly well on Google Colab or equivalent.


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.


I have tried reproducing the papers, with mixed success. I do not share your sentiment.




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