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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 次观看 • 3 年前 •via X (Twitter)

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Arthur Allshire 的头像

Arthur Allshire3 年前

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

Arthur Allshire 的头像

Arthur Allshire3 年前

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.

Arthur Allshire 的头像

Arthur Allshire3 年前

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

Maggie 的头像

Maggie3 年前

@petrenko_ai damn this is so cool

Jonathan A. Michaels 的头像

Jonathan A. Michaels3 年前

@petrenko_ai Very cool

David Hoeller 的头像

David Hoeller3 年前

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

Dennis Anthony 的头像

Dennis Anthony3 年前

@petrenko_ai Super cool work Arthur!!

Zhihao Liu 的头像

Zhihao Liu3 年前

@petrenko_ai 👍

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