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Aviral Kumar

@aviral_kumar2 • 5,816 subscribers

Assistant Professor of CS & ML at @CarnegieMellon. PhD from UC Berkeley.

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🚨Current scalable RL algos train a policy w/o value func, which is limiting with learning in open-ended, non-stationary, dynamic environments. But, how to scale value-based RL with more data/compute is unclear... Not anymore: presenting scaling laws for value-based RL 🧵⬇️

🚨Current scalable RL algos train a policy w/o value func, which is limiting with learning in open-ended, non-stationary, dynamic environments. But, how to scale value-based RL with more data/compute is unclear... Not anymore: presenting scaling laws for value-based RL 🧵⬇️

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🚨🚨New paper: if you want robots to do bimanual long-horizon tasks well, try RaC: a human in-the-loop data collection protocol that naturally amplifies Recovery & Correction behaviors + trains on them. 📈 data efficiency 10x vs prior results (+many nice properties). 🧵⬇️
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🚨🚨New paper: if you want robots to do bimanual long-horizon tasks well, try RaC: a human in-the-loop data collection protocol that naturally amplifies Recovery & Correction behaviors + trains on them. 📈 data efficiency 10x vs prior results (+many nice properties). 🧵⬇️

Aviral Kumar

25,771 просмотров • 8 месяцев назад

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