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population based training (PBT) is underrated for pushing scale and getting better results in GPU-accelerated RL. Our new work DexPBT lead by Aleksei Petrenko shows how it can be used to train highly dexterous hand-arm manipulation in up to 46 DoF systems.

Arthur Allshire

2,553 subscribers

13,569 просмотров • 3 лет назад •via X (Twitter)

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Комментарии: 8

Фото профиля Arthur Allshire

Arthur Allshire3 лет назад

PBT runs a genetic algorithm on top of basic RL. This does multiple things: gives more diversity in the initial policies (reducing intra-seed variability), helps find better hyperparameters, and counteracts the instability that plagues harder RL environments even with good HPs

Фото профиля Arthur Allshire

Arthur Allshire3 лет назад

one nice thing about DexPBT's implementation of PBT is it is decentralised -- only need one shared folder between runs. Slurm, local multi-GPU machine, NGC, other cluster software -- our implementation doesn't care.

Фото профиля Arthur Allshire

Arthur Allshire3 лет назад

this work gives me hope for RL in increasingly complex embodiments and scenarios. PBT works in other robotic environments... stay tuned ;) video - paper - thanks to collaborators @petrenko_ai @ankurhandos @viktor_m81 @gavrielstate

Фото профиля Maggie

Maggie3 лет назад

@petrenko_ai damn this is so cool

Фото профиля Jonathan A. Michaels

Jonathan A. Michaels3 лет назад

@petrenko_ai Very cool

Фото профиля David Hoeller

David Hoeller3 лет назад

@petrenko_ai That’s probably the most impressive manipulation work I have ever seen, congrats!

Фото профиля Dennis Anthony

Dennis Anthony3 лет назад

@petrenko_ai Super cool work Arthur!!

Фото профиля Zhihao Liu

Zhihao Liu3 лет назад

@petrenko_ai 👍

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