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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: Dec. 11, 16.00-17.00 (Amsterdam Time Zone)
Title: "Is My RL Algorithm a Good Tool?" - What Evaluation Strategies Tell Us About Our Algorithms
Link: Zoom Link - Click Here
Abstract: Like many other fields, RL has specific benchmarking and evaluation procedures to measure performance of new and existing algorithms. But how does the way we set up this comparison e.g. (in terms of fixed benchmarking settings and scope of tuning) shape what our evaluation actually says about our algorithms? This talk will explore several possible high-level goals of RL research, like to discover general learning mechanisms or to provide reliable tools for practical use, and how these goals should shape the way we evaluate RL algorithms. By mindfully utilizing different sources of randomness and hyperparameter tuning settings, we can design more expressive experiments that reflect better on the different downstream purposes of our research.
Schedule
Schedule
Talks are always online and take place between 16.00-17.00 (Amsterdam Time Zone).
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 Mar 13: Andrea Tirinzoni (Meta FAIR) Pre-training Behavioral Foundation Models via Zero-shot Reinforcement Learning
Thu May 8: Qiyang Li (UC Berkeley) Leveraging unlabeled task-agnostic offline data for efficient online exploration
Thu June 12: Anikait Singh (Stanford University) Towards Scalable RL Machinery for LLM Post-Training
Thu Dec 11: Theresa Eimer (Leibniz University of Hannover) "Is My RL Algorithm a Good Tool?" - What Evaluation Strategies Tell Us About Our Algorithms
Thu Jan 8: Andrew Wagenmaker (UC Berkeley)
If you have any questions about the seminar series, feel free to contact:
Zhao Yang: z.yang(at)liacs.leidenuniv.nl
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