BeNeRL Seminar Series
The BeNeRL Seminar Series are monthly online talks by RL researchers from all over the world. The intention is to primarily give a platform to advanced PhD students and early career researchers to 1) display their work and 2) share their practical RL experience (i.e., how do you manage large-scale RL experiments as a new researcher in the field, a topic that is often skipped in talks). We maintain a summary of the main advice on experimentation.
The seminar is online and takes place on every second Thursday of the month, 16.00-17.00 (CET)
(unless there is a conflict with an important machine learning conference, when we try to shift by one week)
Date: January 9, 16.00-17.00 (CET)
Title: Diffusion Models for Decision Making
Link: Zoom Link - Click Here
Abstract: Diffusion models (DMs) are powerful generative models that demonstrate promising performance across various domains. Motivated by their remarkable capability in complex distribution modeling and conditional generation, researchers have developed a series of works applying DMs for decision-making tasks. I will introduce the various roles that DMs play in decision-making tasks from a unified perspective. I will delve into the latest advancements in aligning human feedback (AlignDiff), and increasing decision frequency (DiffuserLite) using DMs in decision-making tasks. Finally, I will explain the framework of the unified decision diffusion models (CleanDiffuser).
Schedule
Talks are always online and take place between 16.00-17.00 (CET).
2023
Thu Oct 12: Benjamin Eysenbach (Princeton) Connections between Reinforcement Learning and Representation Learning
Thu Nov 16: Cansu Sancaktar (Max Planck Institute) Playful Exploration in Reinforcement Learning
2024
Thu Feb 8: Pierluca D'Oro (Mila) On building World Models better than reality
Thu April 11: Minqi Jiang (Google Deepmind) Learning Curricula in Open-Ended Worlds
Thu May 16: Edward Hu (University of Pennsylvania) The Sensory Needs of Robot Learners
Thu June 13: Nicklas Hansen (UC San Diego) Data-Driven World Models for Robots
Thu Sep 12: Daniel Palenicek (TU Darmstadt) Sample Efficiency in Deep RL: Quo Vadis? (slides)
Thu Oct 10: Ademi Adeniji (UC Berkeley) Reinforcement Learning Behavioral Generalists - Top-Down and Bottom-Up (slides)
Thu Nov 14: Tal Daniel (Technion) Particles to Policies: Object-Centric Learning in Pixel-Based Decision Making (slides)
Thu Dec 19: Hojoon Lee (KAIST AI) Designing Neural Network Architecture for Deep Reinforcement Learning (slides)
2025
Thu Feb 13: Andrea Tirinzoni (Meta FAIR)
If you have any questions about the seminar series, feel free to contact:
Zhao Yang: z.yang(at)liacs.leidenuniv.nl