
Technion - Reinforcement Learning Research Labs
61 posts

Technion - Reinforcement Learning Research Labs
@Technion_RL
Official account covering research performed in the various #ReinforcementLearning labs at the @TechnionLive
Haifa, Israel Katılım Ekim 2019
15 Takip Edilen770 Takipçiler

Deep networks combined with finite memory poses a problem for bandit-based algorithms.
In this blog post, Ofir explains their method for overcoming the drift in the learned features and avoiding catastrophic forgetting!
rlrl.net.technion.ac.il/2021/06/24/onl…
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Interested in learning how to detect reward deterioration?
Check out the blog post by Ido Greenberg!
rlrl.net.technion.ac.il/2021/06/24/det…
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Check out @carmelrabinovi1 's new work! Constrastive domain randomization for sim-to-real transfer!
Carmel Rabinovitz@CarmelRabinovi1
Excited to share our new #ICRA2021 paper on “Contrastive Domain Randomization” (CDR) with my amazing collaborators @nikogrupen, and @AvivTamar1: Website: sites.google.com/view/contrasti… Paper: arxiv.org/abs/2103.11144 Code: github.com/carmelrabinov/… #robotics #ContrastiveLearning #AI
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Action Redundancy in Reinforcement Learning - UAI
Nir Baram, @guytenn and @MannorShie
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Bandits with Partially Observable Confounded Data - UAI
@guytenn, @ShalitUri, @MannorShie and Yonathan Efroni
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It's been a while without updates. Let's cover some of the recent publications from the various RL groups at the @TechnionLive !
Thread.
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Awesome blog post by Orr, Omer and Raveh!
Interested in RL and robotics? Certainly worth checking this out!
Orr Krupnik@orrkrup
What happens when you set out to reproduce a "reproducible robotics benchmark"? Omer and Raveh tried it out with #REPLAB! Their findings are detailed in the new blog post at @Technion_RL. TL;DR: it takes some work, but it's definitely possible!
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Work by @BeloStav, linkedin.com/in/philip-kors…, @TZahavy, @tesslerc, @MannorShie
To be featured in a special issue on RL for Real Life @Springer @MLJ_Social, by (@yuxili99, @AlborzGr, @LihongLi20, @CsabaSzepesvari, Tao Wang). link.medium.com/9dkamunnCcb
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To evaluate our algorithm we use the MIMIC-III data set mimic.physionet.org @MIT_IMES. We focus on Sepsis treatment, which recently emerged as a benchmark for RL algorithms, and achieve new SOTA results.

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Interested in #RealWorld applications of #ReinforcementLearning?
Check out our recent work, “Inverse Reinforcement Learning in Contextual MDPs” where we learn from clinicians how to treat patients with Sepsis!
Paper: bit.ly/381H6Da
Code: github.com/coirl/coirl_co…
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