📣 Reinforcement Learning Summer School is returning to Milan in 2026! Co-organized with @ellisunitmilan.bsky.social & designed for Master's and PhD students on RL theory, multi-agent systems, RL & LLMs, real-world applications... 📍 Milan 🇮🇹 📅 3-12 June ⏰ Apply by 27 March 🔗 https://bit.ly/4b2Plhp

At NeurIPS, we presented asymmetric RL algo: Asymmetric Advantage Weighted Regression (AAWR). This time, the goal is to learn a policy pi(a | z) whose input z = f(h) is not necessarily Markovian. It is useful in robotics, for example. So, how to adapt RL in POMDP to non Markovian input? 🧵

At #EWRL, we presented 4 papers, which we summarize below. - A Theoretical Justification for AsymAC Algorithms. - Informed AsymAC: Theoretical Insights and Open Questions. - Behind the Myth of Exploration in Policy Gradients. - Off-Policy MaxEntRL with Future State-Action Visitation Measures.

Had an amazing time presenting my research @cohereforai.bsky.social yesterday 🎤 In case you could not attend, feel free to check it out 👉 youtu.be/RCA22JWiiY8?...

Such an inspiring talk by @arkrause.bsky.social at #ICML today. The role of efficient exploration in Scientific discovery is fundamental and I really like how Andreas connects the dots with RL (theory).

At #ICML2025, we will present a theoretical justification for the benefits of « asymmetric actor-critic » algorithms (#W1008 Wednesday at 11am). 📝 Paper: hdl.handle.net/2268/326874 💻 Blog: damien-ernst.be/2025/06/10/a...

🌟🌟Good news for the explorers🗺️! Next week we will present our paper “Enhancing Diversity in Parallel Agents: A Maximum Exploration Story” with V. De Paola, @mircomutti.bsky.social and M. Restelli at @icmlconf.bsky.social! (1/N)

Last week, I gave an invited talk on "asymmetric reinforcement learning" at the BeNeRL workshop. I was happy to draw attention to this niche topic, which I think can be useful to any reinforcement learning researcher. Slides: hdl.handle.net/2268/333931.

Two months after my PhD defense on RL in POMDP, I finally uploaded the final version of my thesis :) You can find it here: hdl.handle.net/2268/328700 (manuscript and slides). Many thanks to my advisors and to the jury members.

📝 Our paper "A Theoretical Justification for Asymmetric Actor-Critic Algorithms" was accepted at #ICML! Never heard of "asymmetric actor-critic" algorithms? Yet, many successful #RL applications use them (see image). But these algorithms are not fully understood. Below, we provide some insights.

📢 Deadline extended! Submit your work to EWRL — now accepting papers until June 3rd AoE. This year, we're also offering a fast track for papers accepted at other conferences ⚡ Check the website for all the details: euro-workshop-on-reinforcement-learning.github.io/ewrl18/

Slydst, my Typst package for making simple slides, just got its 100th star on Github. While I would not advise using Typst for papers yet, its markdown-like syntax allows to create slides in a few minutes, while supporting everything we love from LaTeX: equations. github.com/glambrechts/...

📣 Hiring! I am looking for PhD/postdoc candidates to work on foundation models for science at @ULiege, with a special focus on weather and climate systems. 🌏 Three positions are open around deep learning, physics-informed FMs and inverse problems with FMs.

Check our work on max entropy RL! We introduce an off-policy method to maximize the entropy of the future state-action visitation distribution, leading to policies that explore effectively and achieve high performance 🎯 Link 📑 arxiv.org/abs/2412.06655 #RL #MaxEntRL #Exploration

How come I didn't know about this BeNeRL seminar series? It focuses on practical RL and seems really great! www.benerl.org/seminar-seri... I would have loved to hear Benjamin Eysenbach, Chris Lu and Edward Hu... Next one is on December 19th.

Hi Bluesky! I'm a PhD student researching #RL in #POMDP. If you're also interested in: - sequence models, - world models, - representation learning, - asymmetric learning, - generalization, I'd love to connect, chat, or check out your work! Feel free to reply or message me.