Today our machine learning and distributed computing teams, along with Google Brain, are releasing Launchpad, a system for building distributed machine learning algorithms. Visit https://t.co/Ua86xIIx7s for the code, examples, and some distributed RL agents!
Today we released Launchpad, which is what we use for running almost all of our distributed RL experiments at DeepMind. This has been a great project to work on, and we hope the community finds it useful!
On behalf of the entire Google Research & @GoogleAI communities, I'm excited to share an overview of some of our research in 2020.
Thanks to everyone who helped make this work possible!
https://t.co/EYVeQ7PfO4
⚡️🔋 Supercharge your recommenders
Built-in fast approximate retrieval and improved feature interaction learning are now available in the new release of TensorFlow Recommenders #TFRS.
Learn more → https://t.co/WUCFLWRX5m
The launch of @TensorFlow ~5 years ago this week was a pretty exciting milestone for a lot of us. We all gathered in a conference room early one morning to watch the original blog post announcing it go live & people start downloading it:
https://t.co/LZpunwFWfx
What objectives can an intelligent agent optimize?
In this 3 year collab, we categorized the possible objs. APD is a unifying principle that explains repr learning, reward, infogain exploration, empowerment, skill discovery, and niche seeking.
https://t.co/Ja4xxea1M9
👇 Thread
🎉 Papers with Code partners with arXiv! Code links are now shown on arXiv articles, and authors can submit code through arXiv. Read more: https://t.co/kO6zhWAWGH
Quite compelling SAC readme with a bunch of interesting empirical data: https://t.co/LECVdDS3KH
from @summeryue0@tobyjboyd Thank you for putting this together.
Looks like Sanjay and I have a few TODO items :) :
https://t.co/ED4HQQcHe1
We are pair programming tomorrow, but already planning to work on something else.
☝ We have a recommendation for that
Introducing TensorFlow Recommenders, an open-source package that makes building, evaluating, and serving recommender models easy. Find recommendations for movies, restaurants, and much more!
Get started → https://t.co/ydGyCZgMxT
In our new paper we scale model-based reinforcement learning to the gym humanoid by using short-horizon model rollouts followed by a learned model-free value estimate.
Paper: https://t.co/UytqwsqKdz
Videos: https://t.co/cjZ60UJg6Z
With @sam_d_stanton @denisyarats@andrewgwils