Aravind Srinivas: New paper - CURL: Contrastive Unsupervised Representations for RL! We use the simplest form of contrastive learning (instance-based) as an auxiliary task in model-free RL. SoTA by *significant* margin on DMControl and Atari for data-efficiency. https://arxiv.org/abs/2004.04136 https://t.co/3ZSfzmxemE
12 replies, 956 likes
hardmaru: Contrastive unsupervised features might be the ingredient that makes RL much more sample efficient. Nice results demonstrating the method for Atari and continuous control (from pixels) tasks.
@Aravind7694 @MishaLaskin @pabbeel
1 replies, 207 likes
Michael Laskin: Can pixel-based RL be as data-efficient as state-based RL? We show for the first time that the answer is yes, new work with @Aravind7694 and @pabbeel
website 👉 http://mishalaskin.github.io/curl
code 👉 https://github.com/MishaLaskin/curl
2 replies, 104 likes
hardmaru: Another datapoint: https://twitter.com/Aravind7694/status/1248049713149906945
1 replies, 34 likes
Alex Nichol: Amazing work! This is a very important moment for RL.
1 replies, 31 likes
Max Jaderberg: Simple and effective, fantastic! 👏👏
0 replies, 30 likes
roadrunner01: CURL: Contrastive Unsupervised Representations for Reinforcement Learning
github: https://github.com/MishaLaskin/curl https://t.co/nML3KnRzKr
0 replies, 18 likes
Aravind Srinivas: New paper - Reinforcement Learning with Augmented Data (RAD)! Data augmentation *alone* can achieve SoTA on DMControl and test-time generalization on ProcGen!
Project Page: https://mlaksin.github.io/rad
Code: https://github.com/MishaLaskin/rad https://t.co/VtA2oHRBvY
1 replies, 11 likes
Timothy O'Hear: Fascinating results, would love to a @CShorten30 or @ykilcher video on this paper :-)
1 replies, 8 likes
rewon: Simple, powerful, and efficient!
0 replies, 4 likes
Daisuke Okanohara: CURL performs unsupervised contrastive representation learning simultaneously with pixel-based off-policy RL training, which enables to reduce the required training samples significantly. Simple and very effective for data-efficient RL. https://arxiv.org/abs/2004.04136
0 replies, 4 likes
ALife Papers: CURL: Contrastive Unsupervised Representations for Reinforcement Learning
"CURL extracts high-level features from raw pixels using contrastive learning and performs off policy control on top of the extracted features."
0 replies, 1 likes
Xander Steenbrugge: When applied to RL, these techniques yield vastly more sample-efficient agents, but are still quite far off human data efficiency...
The open question is how to learn generalizable priors that transfer across a wide range of environments & tasks!
1 replies, 0 likes
Found on Apr 09 2020 at https://arxiv.org/pdf/2004.04136.pdf