Really cool work! 🙌
22.02.2026 13:50
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Really cool work! 🙌
Reading group TOMORROW 3-4pm UK!
Joe Marino (Google DeepMind) will present SIMA 2, a generalist embodied agent designed to operate across a wide range of 3D virtual worlds 🌎
Join here: edinburgh-rl.github.io/reading-group
Reading group TODAY 2pm BST!
"Despite years of research in offline reinforcement learning, the field has failed to deliver major breakthroughs..."
Matthew Jackson and Jarek Liesen (Oxford) will present Unifloral - unified implementations and evaluations for offline RL: arxiv.org/abs/2504.11453
Happening now - Exhibit Hall C,D,E poster #404
I heard there will be good vibes at this poster 🤙
First time in a Waymo. Honestly, a pretty surreal experience! Surprised by how smooth the ride was and how quickly I felt comfortable in the car 😮
If you are around and want to chat about multi-agent systems (MARL, agentic systems), open-endedness, environments, or anything related, please let me know! 🎉
Thrilled to present HyperMARL at #NeurIPS2025 in San Diego next week! 🚀 (Amos will present at
@euripsconf.bsky.social too.)
TL;DR: Coupling obs and agent IDs can hurt performance in MARL. Agent-conditioned hypernets cleanly decouple grads and enable specialisation.
📜: arxiv.org/abs/2412.04233
I think most people judge reputation from high-level things e.g. num of accepted papers, and very few people actually read these papers. This means you can game the system with LLM generated papers with little consequences, and this makes things frustrating for everyone.
Reading group today at 2pm BST!
We are starting our NeurIPS series with Sable and Oryx, sequence models for scalable multi-agent coordination from the RL Research Team at InstaDeep. 🚀
Papers:
- Sable: bit.ly/3Lme7jH
- Oryx: bit.ly/47GJb4T
Meeting:
- bit.ly/3JoEbtU
📢 RL reading group Thursday @ 16:00 BST 📢
Speaker: Alex Lewandowski
Title: The World Is Bigger: A Computationally-Embedded Perspective on the Big World Hypothesis 🌍
Details: edinburgh-rl.github.io/reading-group
Refreshing to see posts like this compared to "we have 15 papers accepted at X" 🙌
None of our impactful papers have had an easy path through traditional venues.
Most cited paper? Rejected four times.
Most impactful paper? Poster at a conference.
But none of it matters because arxiv makes everything work
Great first couple of days at DLI @deeplearningindaba.bsky.social in Kigali 🇷🇼, some highlights include amazing talks talks by @verenarieser.bsky.social and Max Welling, great pracs and tuts, and of course the opening party ( before the rain 😢) 🎉 #DLI2025
We’re excited to unveil the first #DLI2025 lineup of tutorials and practicals:
✨ Machine Learning Foundations
✨ Generative Models & LLMs for African languages
All tutorial content will also be available online after the Indaba. Don’t miss out, subscribe here 👉 lnkd.in/eCgXRqsV
🙌🎉
🇨🇦 Heading to @rl-conference.bsky.social next week to present HyperMARL (@cocomarl-workshop.bsky.social) and Remember Markov (Finding The Frame Workshop).
If you are around, hmu, happy to chat about Multi-Agent Systems (MARL, agentic systems), open-endedness, environments, or anything related! 🎉
We are thrilled to announce our next keynote speaker
@wellingmax.bsky.social, Professor at the University of Amsterdam, Visiting Professor at Caltech and CTO & Co-Founder of CuspAI.
Catch his talk “How AI could transform the sciences” on August 18 at 4:30 PM GMT+2.
#DLI2025
RL reading group TODAY @ 15:00 BST 🔥
Speaker: Cam Allen (Postdoc, UC Berkeley)
Title: The Agent Must Choose the Problem Model
Details: edinburgh-rl.github.io/reading-group
Always nice to see when simpler methods + good evaluations > more complicated ones. 👌
Reading group is back for those interested in RL/MARL/agents/open-endedness and alike... First session today at 3pm BST, @mattieml.bsky.social is presenting the Simplifying TD learning/PQN paper. 🎉 Meeting link: bit.ly/4lfdaGR Sign up: bit.ly/40xNQDR
Hello world! This is the RL & Agents Reading Group
We organise regular meetings to discuss recent papers in Reinforcement Learning (RL), Multi-Agent RL and related areas (open-ended learning, LLM agents, robotics, etc).
Meetings take place online and are open to everyone 😊
This has happened to me too many times 🤦♂️ Also doesn't help that Jax and PyTorch use different default initialisations for dense layers.
Well done & well deserved!! 🎉🎉 It has been awesome to see this project evolve from the early days.
The Edinburgh one will be back and running soon. We are just updating the website and other things. There is this form for people interested - forms.gle/DAbkpN9b4cUt...
Forgot to also add ⚡ quickstart link for people who like to experiment on notebooks: github.com/KaleabTesser...
Thanks for checking it out! 👍 Good point, there might be an interesting link between MoEs and hypernets. We used hypernets since they're simpler (no need to pick or combine experts), and maximally expressive (gen weights directly).
Lol yes, will had a .gitignore, missed it when copying things over.
🎯 TL;DR: HyperMARL is a versatile approach for adaptive MARL -- no changes to the RL objective, preset diversity, or seq. updates needed. See paper & code below!
Work with Arrasy Rahman, Amos Storkey & Stefano Albrecht.
📜: arxiv.org/abs/2412.04233
👩💻: github.com/KaleabTessera/HyperMARL
⚠️ Limitations (+opportunity): HyperMARL uses vanilla hypernets, which can inc. param. count esp. MLP hypernets. In RL/MARL this matters less (actor-critic nets are small), and params grow ~const with #agents, so scaling remains strong. Future work could explore chunked hypernets.
🔎 We also do ablations and see the importance of the decoupling and the simple initialisation scheme we follow.
📊 We validate HyperMARL across various diverse envs (18 settings; up to 20 agents) and find that it achieves competitive mean episode returns compared to NoPS, FuPS, and modern diversity-focused methods -- without using diversity losses, preset diversity levels or seq. updates.