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

🇨🇦 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 also do ablations and see the importance of the decoupling and the simple initialisation scheme we follow.

💡To address the coupling problem, we propose 𝐇𝐲𝐩𝐞𝐫𝐌𝐀𝐑𝐋: a method that explicitly 𝐝𝐞𝐜𝐨𝐮𝐩𝐥𝐞𝐬 obs- and agent-conditioned gradients with hypernetworks. This means obs grad noise is avg. per agent (Zᵢ) before applying agent-cond. grads (Jᵢ) -- unlike FuPS, which entangles both.

🔬 We isolate FuPS’s failure in matrix games: shared policies struggle when agents need to act differently. Inter-agent gradient interference is at play -- especially when obs and agent IDs are 𝐜𝐨𝐮𝐩𝐥𝐞𝐝. Surprisingly, using only IDs (no obs) performed better and reduced interference.

📜🤖 Can a shared multi-agent RL policy support both specialised & homogeneous team behaviours -- without changing the learning objective, requiring preset diversity levels or sequential updates? Our preprint “𝘏𝘺𝘱𝘦𝘳𝘔𝘈𝘙𝘓: 𝘈𝘥𝘢𝘱𝘵𝘪𝘷𝘦 𝘏𝘺𝘱𝘦𝘳𝘯𝘦𝘵𝘸𝘰𝘳𝘬𝘴 𝘧𝘰𝘳 𝘔𝘶𝘭𝘵𝘪-𝘈𝘨𝘦𝘯𝘵 𝘙𝘓” explores this!

Reminder of how common paper rejections are, even for people like @teorth.bsky.social 💡

Coffee shop + Sunday + Admin = elite combo. Honestly, I don't know how I managed a to-do list before this.