On average I have a good score, but it has happened to me before to have 3/4 reviewers accepting the paper, and 1 negative reviewer convincing the AC to reject.

And now I got the classic rebuttal response:

"I have no concerns with the paper, all the theory is great, but since you did not run experiments in expensive domains with image-based environments, I will not increase my score".

The goal of experiments is to validate the claims! Not to beat Atari!

Finally, reporting only IQM may compromise scientific transparency and fairness, as it can mask poor or unstable performance. Agarwal et al. (2021), who introduced IQM in this context, recommend using it in conjunction with other statistics rather than as a standalone measure.

Yes, Interquartile Mean (IQM) is a robust statistic that reduces the influence of outliers. But it does not by itself provide a clear and fair analysis of performance. In particular, IQM does not capture the full distribution of returns and may hide important information about variability and risk.

This work was done during my time as an intern at Disney Research Zürich. It was amazing and really fun to develop this idea with the Robotics Team!

Check out AMOR now on arXiv:

Paper: arxiv.org/abs/2505.23708
Full Video: youtube.com/watch?v=gQid...

#SIGGRAPH2025 #RL #robotics

A base policy with uniform weights might fail on challenging motions, but with a few weight tweaks, it nails them. Like this double spin. 🌀😵‍💫

Curious how tuning weights mid-motion can help improve the sim-to-real gap and unlock dynamic, expressive behaviors?

AMOR trains a single policy conditioned on reward weights and motion context, letting you fine-tune the reward after training.
Want smoother motions? Better accuracy? Just adjust the weights — no retraining needed!

We are excited to share our #SIGGRAPH2025 paper,

“AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning”!
Lucas Alegre*, Agon Serifi*, Ruben Grandia, David Müller, Espen Knoop, Moritz Baecher

Thank you, Peter! :)

Thank you!
link.springer.com/article/10.1...
This paper is a great start point!

Thank you! 😊

Finally, I would like to thank my advisors, Prof. Ana Bazzan and Prof. Bruno C. da Silva; Prof. Ann Nowé who received me at VUB for my PhD stay; and Disney Research Zürich, where I interned.

I am very grateful to everyone with that I had the chance to collaborate in all such amazing projects! 💙

I believe all these contributions open room for many interesting ideas for multi-policy RL methods. Especially in transfer learning (SFs&GPI) and multi-objective RL settings! 🚀

* MO-Gymnasium (github.com/Farama-Found...) is a library of MORL environments; and

* MORL Baselines (github.com/LucasAlegre/...) is a library of MORL algorithms.

Both have become standards in MORL research and have over 100k downloads in the past year!

Besides the theoretical and algorithmic contributions, we also introduced an open-source toolkit for MORL research!

NeurIPS D&B 2023 Paper - openreview.net/pdf?id=jfwRL...

Next, we further explored how to leverage approximate models of the environment to improve zero-shot policy transfer. Our method, ℎ-GPI, interpolates between model-free GPI and fully model-based planning as a function of the planning horizon ℎ.

NeurIPS 2023 Paper - openreview.net/pdf?id=KFj0Q...