Théo Vincent

@theo-vincent.bsky.social

PhD student working on RL 🤖 @DFKI & @ias-tudarmstadt.bsky.social | Master MVA @ENS_ParisSaclay & ENPC 🎓 https://www.ias.informatik.tu-darmstadt.de/Team/TheoVincent

🥳 Congratulations!! It was a pleasure to work with Tim during his master's thesis! More to come soon⏳

Intelligent Autonomous Systems@ias-tudarmstadt.bsky.social · 2w ago

🏆 Tim Faust received the Freudenberg award for his thesis on Model Simplification in Value-based Deep RL!! 🎉 Tim did his thesis at our lab supervised by @theo-vincent.bsky.social This award rewards the best master's thesis across all fields of research TU Darmstadt with 10k€

We are proud to have an amazing line-up of speakers! They will present their works, which incorporate the constraint that the world is bigger than the agent and impossible to anticipate, observe, or model perfectly. We are also looking forward to the panel discussion!

Bild

Working with constrained agents in complex environments? Do not hesitate to submit your latest work to this workshop! See you in Montréal @rl-conference.bsky.social 🇨🇦

RL in Big Worlds@rlcbigworlds.bsky.social · 5mo ago

RL in Big Worlds is a workshop at @rl-conference.bsky.social about ideas that enable agents to achieve goals in environments vastly more complex than themselves. This requires giving agents the ability to learn continually and use approximate value functions, models, and policies effectively.

A bunch of us are organizing a workshop at RLC. If your goal is to develop algorithms that allow agents to learn from complex data streams without relying on human data and human designers, then this workshop would be a good fit.

RL in Big Worlds@rlcbigworlds.bsky.social · 5mo ago

RL in Big Worlds is a workshop at @rl-conference.bsky.social about ideas that enable agents to achieve goals in environments vastly more complex than themselves. This requires giving agents the ability to learn continually and use approximate value functions, models, and policies effectively.

Really excited to organize this workshop! Many works overlook the complexity ratio between the agent and its environment, often leading to overpowered agents. If we want agents to learn continuously in the wild, we need to care about this ratio!

RL in Big Worlds@rlcbigworlds.bsky.social · 5mo ago

RL in Big Worlds is a workshop at @rl-conference.bsky.social about ideas that enable agents to achieve goals in environments vastly more complex than themselves. This requires giving agents the ability to learn continually and use approximate value functions, models, and policies effectively.

Benchmarking always takes a ton of time😮‍💨 and we often hear about it🗣️ But we rarely report the carbon footprint of experiments, which better reflects their weight! Here is the electricity emission of the experiments in each paper of my PhD👇

Quick reminder for everyone grinding on their RLC 2026 papers, only ~3 weeks to go! The submission site opens in just a few days (Feb 17). Deadlines: ⏳ March 1 (AoE): Abstract Submission ⏳ March 5 (AoE): Full Paper Submission Good luck with the final changes!

Post nicht verfügbar.

🧵 Accepted at @iclr-conf.bsky.social! Target networks stabilize bootstrapping in RL 🛡️ But induce slow-moving targets 🐢 Online networks adapt fast ⚡ But can diverge with function approximation 💥 𝗠𝗜𝗡𝗧𝗢 🌿 uses the online network 𝗼𝗻𝗹𝘆 𝗶𝗳 𝗶𝘁 𝗰𝗮𝗻 — yielding faster 𝘢𝘯𝘥 more stable RL. Here’s how 👇

🎤 Announcing the 3rd workshop on Reinforcement Learning in Mannheim 🎤 We have an amazing lineup of speakers: @Mathieugeist, @gio_ramponi, Theresa Eimer, @SarahKeren_, @araffin2, @c_rothkopf, and @AdrienBolland ⏰ Friday 6th February 📍University of Mannheim

Bild

Sparse network -> sparse poster I will be presenting Eau De Q-Network today @rldmdublin2025.bsky.social Feel free to come and exchange at Poster #28 🎤 bsky.app/profile/theo...

Bild
Théo Vincent@theo-vincent.bsky.social · last yr.

Very excited to present 🎉Eau De Q-Network🎉 on Thursday @rldmdublin2025.bsky.social Poster #28 🔍Eau De Q-Network gradually prunes the network weights at the agent's learning pace, ultimately reaching a final sparsity level that is discovered by the algorithm!🔎 👉📰 arxiv.org/pdf/2503.01437