Top Tweets for #RLlib
New post by @Maurice_Brg
Understanding Iterations in Ray RLlib
https://t.co/RLHngBxYct
#aws #machinelearning #ray #rllib
We listened to our users and are bringing #DreamerV3's implementation to #RLlib. On our new RLModule / Learner API. A very exciting algorithm indeed. Takes the pain of hyper-paremeter tuning away. Early signs are super promising. Stay tuned for more info.
Last week, we released Ray 2.3. ICYMI:
⭐️ Observability enhancements
⭐️ Ray Dataset Streaming
⭐️ Boost in Ray core performance
⭐️Gym/Gymnasium library in #RLlib
⭐️ Support ARM & Python 3.11
⭐️ Support multiple applications in Ray Serve
https://t.co/55tRgBCGGf
We are happy to announce that for #RLlib 2.3 we have moved away from the old @OpenAI gym APIs to @FaramaFound 's gymnasium API. Here is what you need to know to about this transition:
Ray 2.3.0 Released with:
⭐️ Observability enhancements
⭐️ Ray Dataset Streaming
⭐️Boost in Ray core performance
⭐️Gym/Gymnasium library in #RLlib
⭐️ Support ARM & Python 3.11
⭐️ Support multiple applications in Ray Serve (developer preview)
https://t.co/55tRgBC8QH
Now. Jungong and @CyrusHakha from @anyscalecompute #RLlib tutorial @odsc
#ODSCWest in-person at SFO.

Excited to announce JunGong and @CyrusHakha will be giving a hands-on workshop using @anyscalecompute @raydistributed #RLlib. Thursday Nov 3, 11am PT @odsc #ODSCWest. 👋🏽 Hope to see you there! Pro-tips in my post below.
https://t.co/uRUAlGdEa8
@anyscalecompute @raydistributed team draws a huge crowd at @ACMRecSys hands-on tutorial on #RL & #RLlib
WOW the tutorial room for the Hands-on Reinforcement Learning workshop is packed at @ACMRecSys 😱 @CyrusHakha @cbergman @anyscalecompute this is a popular one! #RecSys2022

Thank you everyone for attending our tutorial by @CyrusHakha @narayan_avnish and myself! Hope you were able to learn more about @raydistributed @anyscalecompute #RLlib, please let me know if I can help!
WOW the tutorial room for the Hands-on Reinforcement Learning workshop is packed at @ACMRecSys 😱 @CyrusHakha @cbergman @anyscalecompute this is a popular one! #RecSys2022

Nice interview with @sven_mika , fearless leader/committer of #RLlib, about history of @raydistributed, built for RLlib! Supports both TF1/TF2 and PyTorch, runs distributed on any compute (single machine or cloud cluster), many algorithms model-based, model-free, online, offline
Episode 37
@sven_mika of @anyscalecompute on RLlib present and future, Ray and Ray Summit 2022, applied RL in games / finance / recsys, and more!
https://t.co/oNgT2Wkxr0
With Ray 2.0, our goal is to make #RLlib even more intuitive, easy to extend, and performant for various research and production use cases.
Check out what we have in store at #RaySummit: https://t.co/0ENS8cB9kV

Get hands-on with us next week at #RaySummit!
There’s still time to sign up for our #RLlib training on Aug 22 and create a multiplayer game using RLlib!
https://t.co/brn0FWEE3b

Latest #RLlib office hour now live! @CyrusHakha, Jun, @sven_mika answer questions about multi-agent #ReinforcementLearning for meat #SupplyChain! Also questions about future RLlib API @raydistributed AI Runtime #RayAIR @Ray_Summit_Live (Aug22-24) in SF!
https://t.co/IiuxSs30i3
Did you miss this week's #RLlib office hour? Here's what we covered!
💥 How to train a SAC agent in contentious Action/Obs space
💥 Help debugging getting tensor values for metrics
https://t.co/cZAaRxp92e
👋Everyone welcome next #RLlib office hours! @sven_mika hosting Tues July5 08:00PT. Also check out previous videos of the team answering users questions from @CyrusHakha, Artur, @narayan_avnish https://t.co/aH8w0dWhcM
The next #RLlib office hour is coming up next week on July 5!
Add your #ReinforcementLearning questions for discussion here:
https://t.co/cgFmtRYGgs
ICYMI @raydistributed Community meetup, here're the recordings:
👉 AI/ML-Infused Digital IC Design Workflows on the Hybrid Cloud, @IBMResearch
👉 Pushing boundaries of material design with #RLlib @Stanford
https://t.co/pAkM1jRbtO
👉 Pushing boundaries of material design with RLlib
Using @raydistributed #RLlib and #Tune to minimize the
weight and surface, but remain robust to physical stresses as an #RL problem.

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