🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

I am recruiting for a Waymo-funded PhD studentship in my academic lab at Oxford Comp Sci to work on fundamental challenges in imitation learning for autonomous vehicles. Deadline to apply is 1 March. cs.ox.ac.uk/admissions/gra…

Meta-learning can discover RL algorithms with novel modes of learning, but how can we make them adapt to any training horizon? Introducing our #ICLR2024 work on discovering *temporally-aware* RL algorithms! Work co-led with Chris Lu, in Foerster Lab for AI Research and WhiRL

Come see this fantastic tutorial today on meta-RL by lab members Risto Vuorio and Jacob Beck at #AAAI24!

Real-world agents often can’t interact with their environment… So what if they imagined it? 🤔💭 In our new work (led by Matthew Jackson) we use diffusion models to interactively generate entire sequences of synthetic experience 🤖

Excited to share that our work Bayesian Exploration Networks (BEN) has been accepted at ICML 🍾! BEN is the first model-free Bayesian RL approach that can learn Bayes-optimal policies 🙀 Congrats to @mattiefoxcs and collaborators! arxiv.org/pdf/2308.13049

Exciting updates to Policy-Guided Diffusion! 🎉 PGD was accepted at RL_Conference - see you in Amherst! 📈 For those building on PGD, we just released WandB logs with agent and diffusion model training: api.wandb.ai/links/flair/jo…

1/ 🤖 Learned optimization offers huge potential to automate machine learning! So why doesn't it work well in RL (and how did we fix it)?! I'm excited to share OPEN, our AutoRL Workshop spotlight paper exploring this question! 🧵

Attending #ICML2024 was amazing and full of firsts: My first time presenting a poster, first time giving a talk at a conference and first time sitting on a panel! Many thanks to the AutoRL Workshop organisers for preparing a great workshop about AutoRL!

Check out this fantastic showing by Jacob Beck and Alex Goldie at #ICML2024! 🎙️🔥 They dove deep into the future of automated RL, meta-learning, and LLMs 🤖🔮

🎉🚨 Big news! Our research, Metalic: Meta-Learning In-Context with Protein Language Models, 🧬 won a competition! #NeurIPS2024🤖📚 We advance in-context learning and protein fitness prediction with this paradigm: ✨ Pre-training 🔥 Learning to in-context learn🔥 ✨ Fine-tuning

A new version of the paper Counterfactual Multi-Agent Policy Gradients, that I first published in 2017 with Jakob Foerster, Greg Farquhar and others, is now available on arXiv (arxiv.org/abs/1705.08926).

Excited to share our new survey of in-context reinforcement learning!! arxiv.org/abs/2502.07978 w/ Amir Moeini Jiuqi Wang Jacob Beck Ethan Blaser Shimon Whiteson Rohan Chandra

Big news—our survey paper “A Tutorial on Meta-Reinforcement Learning” is officially published! Meta-RL = learning how to adapt through interaction. It embraces The Bitter Lesson: don’t hardcode agents—train them to adapt on their own arxiv.org/abs/2301.08028 🧵⬇️

🎉 Our Meta-RL survey is now published in Foundations and Trends in Machine Learning! A deep dive into how agents can learn to learn 🤖🧠 Huge kudos to Jacob Beck & Risto Vuorio for leading the charge, and to co-authors Evan Liu, Zheng Xiong, Chelsea Finn & Shimon Whiteson!

Our survey on meta reinforcement learning has now been published by Foundations and Trends in Machine Learning: nowpublishers.com/article/Detail…

🌹 Today we're releasing Unifloral, our new library for Offline Reinforcement Learning! We make research easy: ⚛️ Single-file 🤏 Minimal ⚡️ End-to-end Jax Best of all, we unify prior methods into one algorithm - a single hyperparameter space for research! ⤵️

The best of RL research, brought to Offline RL! 🚀 TL;DR 1. CleanRL-style implementations ⚡️ 2. Rainbow-style algorithm unification 🦾 3. Rliable-style evaluation protocol 🔬 Check out our paper + library!