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The starting states being at different points in the task and having success-guided starting state sampling seem like really neat ideas. I haven't kept up with RL in a while, how novel is this?

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The mechanism is loosely related to curriculum learning, though we argue there are some distinctions. In the methodological companion paper (https://sgs-rl.github.io), we try to get into more details in the related work and baselines including comparison to methods like PLR (also from Deepmind), SFL, and linear handcrafted curriculums over states.

You are giving me some inspiration, though, that it might be good to try to explain the related techniques in a lower-dimensional setting to help teach the concept more effectively! brb

If you're interested in some more details, take a look at this post I made (and am admittedly still polishing) that discusses the method in a low-dimensional space. I am trying to make it more accessible to the community since explaining it for higher-dimensional robot control / hybrid-contact dynamical systems is a whole other thing :)

https://rosarioscalise.com/garage-success-guided-sampling

Thanks for the responses, I'll check it out!

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