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: December 19, 16.00-17.00 (CET)
Title: Designing Neural Network Architecture for Deep Reinforcement Learning
Link: Zoom Link - Click Here
Abstract: While scaling laws have accelerated breakthroughs in computer vision and language modeling, their effects are less predictable in reinforcement learning (RL), where simply “scaling up” data, parameters, and computations rarely guarantees better results. In this talk, I will explore the barriers that make scaling challenging in RL and introduce new architectural designs that alleviate these challenges. I will also discuss future research opportunities that can improve scaling laws in RL.
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 Jan 9: Yifu Yuan (Tianjin University)
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