How can we make Reinforcement Learning (#RL) more deloyable? Can we develop a unified lens for different approaches to tackling this?
Excited to share our survey on Structure in RL, with @yayitsamyzhang and @LindauerMarius
https://t.co/GXOOa7877x
(1/7)
Stoked to announce that BricksRL, our paper on using PyTorch/TorchRL to train the LEGO® robots in the wild has been accepted at @NeurIPSConf as a spotlight paper! 🪢
Final #runconference@RL_Conference (with my brother as special guest)!!
This has been an amazing set of runs, and I think it's the first time we've had so many people attend all of them.
Thanks to everyone who came out!
��🏾⛹🏾💨🤖
How can we make Reinforcement Learning (#RL) more deloyable? Can we develop a unified lens for different approaches to tackling this?
Excited to share our survey on Structure in RL, with @yayitsamyzhang and @LindauerMarius
https://t.co/GXOOa7877x
(1/7)
Super cool talk by Prof. Doshi-Velez at @RL_Conference highlighting the importance of utilising problem structure when deploying RL on real-world settings!
Super excited to attend @RL_Conference in a few days and can’t wait for the amazing discussions!
If anyone needs an overview of all the papers (Main Conf, Journals, Workshops), here's a scraped Excel Sheet: https://t.co/s0SUusFsVK
I’m excited to announce Craftax, a new benchmark for open-ended RL!
⚔��� Extends the popular Crafter benchmark with Nethack-like dungeons
⚡Implemented entirely in Jax, achieving speedups of over 100x
1/
Excited to share our review on the successor representation and two important generalizations: successor features and successor models!
These provide answers spanning AI, cognitive science, and neuroscience for how agents can efficiently reuse their knowledge in new situations