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: October 10, 16.00-17.00 (CET)
Title: Reinforcement Learning Behavioral Generalists - Top-Down and Bottom-Up
Link: Zoom Link - Click Here
Abstract: The success of training large foundation models with scalable, self-supervised objectives has led to significant advancements in AI, particularly in vision and language. In this talk, I argue that while many challenges in general-purpose agentic learning can be mitigated by using these models as black boxes, there remain valuable opportunities for scalable pretraining tailored specifically to the reinforcement learning domain. I will present work from both perspectives: first, showcasing how foundation models trained via conventional methods can enhance decision-making, and second, exploring novel, scalable pretraining approaches that are native to control and hold promise for endowing artificial agents with stronger forms of behavioral generalization.
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
Thu Nov 14: Tal Daniel (Technion)
Thu Dec 19: Hojoon Lee (KAIST AI)
2025
Thu Jan 9: Yifu Yuan (Tianjin University)
If you have any questions about the seminar series, feel free to contact:
Zhao Yang: z.yang(at)liacs.leidenuniv.nl