Wow, I just realized SUMO-RL reached 1,000 stars on GitHub! 🥳 I created SUMO-RL while I was an undergrad, getting familiar with RL. Traffic signal control is a very cool real-world problem in which RL shines. I'm glad that the community still benefits from it! github.com/LucasAlegre/...

GitHub - LucasAlegre/sumo-rl: Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.

Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries. - LucasAlegre/sumo-rl

github.com

It was very fun to present our last paper "Constructing an Optimal Behavior Basis for the Option Keyboard" at NeurIPS this week! Paper: openreview.net/pdf?id=D4gOo... #neurips2025

Sure, only ~2 weeks to review 5 papers for ICLR. I am sure that all reviewers will have sufficient time to write careful and thoughtful reviews in the following weeks, since they have nothing else to do. It is insane to expect a fair reviewing system in these terms.

It is really cool to see our work on multi-step GPI being cited in this amazing survey! :) proceedings.neurips.cc/paper_files/...

Eugene Vinitsky 🍒@eugenevinitsky.bsky.social · last yr.

Reminded again of Kevin Murphy's excellent RL overview: arxiv.org/abs/2412.05265 A lot of the stuff covered here really is at the cutting edge and not compiled so nicely anywhere else

I got the classic NeurIPS reviews "why did you not compare with [completely unrelated method whose comparison would not help support any of the paper's claim]?" Questioning myself whether I should spend my weekend running this useless experiment or if I should argue with the reviewer.

While I really like the paper "Deep Reinforcement Learning at the Edge of the Statistical Precipice" (openreview.net/forum?id=uqv...), I have seen papers evaluating performance using only the IQM metric and claiming that it is a fairer metric than the mean based on this paper, which is simply wrong.

Annoyed by having to retrain your entire policy just because your reward weights did not quite work on the real robot? 🤖 www.youtube.com/watch?v=gQid...

AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning

YouTube video by DisneyResearchHub

youtube.com

I'm really glad to have been selected as one of the ICML 2025 Top Reviewers! Too bad I won't be able to go since my last submission was not accepted, even with scores Accept, Accept, Weak Accept, and Weak Reject 🫠

Last week, I was at @khipu-ai.bsky.social in Santiago, Chile. It was really amazing to see so many great speakers and researchers from Latin America together!

RL is so back! (well, for some of us, it never really left) awards.acm.org/about/2024-t...

Andrew Barto and Richard Sutton are the recipients of the 2024 ACM A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning.

Andrew Barto and Richard Sutton as the recipients of the 2024 ACM A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning. In a series of papers beginning...

awards.acm.org

I am happy to announce that I successfully defended my PhD, entitled “Sample-Efficieny Multi-Task and Multi-Objective Reinforcement Learning by Combining Multiple Behaviors”! 🎉 These last years have been extremely fun, and I am very lucky to have collaborated with and met so many great people😄

Another must read for reinforcement learning. Answers many key questions for researchers; -Do I need multiple training runs? -How do I report model confidence? -And a great section on common mistakes to fend off reviewer 2 🧪 #DRL #reinforcementlearning #AI arxiv.org/abs/2304.01315

Empirical Design in Reinforcement Learning

Empirical design in reinforcement learning is no small task. Running good experiments requires attention to detail and at times significant computational resources. While compute resources available p...

arxiv.org

Anyone else dislike the idea of papers being almost completely rewritten from scratch during ICLR rebuttals? This period should be used to address minor issues. I find a bit unfair authors expecting the reviewers to increase score when half of the relevant results were only shown during rebuttal.