I'm co-organizing a NeurIPS workshop on goal-conditioned RL. If you work on algorithms or applications of goal-conditioned RL (broadly defined), you should consider submitting! For more details, check out the formal call for papers below and the website: https://goal-conditioned-rl.github.io/2023/
Algorithms. We encourage both proposals of new methods, as well as analyses and/or evaluations of existing ones.
Connections between goal-conditioned RL and other ML areas (e.g., representation learning, self-supervised learning, GANs, probabilistic inference, metric learning, duality).
Applications of goal-conditioned decision making. In addition to common decision making tasks (e.g., robotics and games) and goal-conditioned applications (e.g., instruction-following, molecular discovery), we especially encourage work in goal-conditioned domains where GCRL is not (yet) the mainstream strategy
Submission details:
All anonymous submissions will be managed through OpenReview. For video submission, natural voice narration is still considered anonymized, but don't show authors' faces or names.
Format: choose one of the following:
A report in the NeurIPS style with at most 2 pages (no limit on appendices)
A video (e.g., a narrated slide deck, a video of a talk) in mp4 format lasting at most 5 minutes.
One author of each submission volunteers serves as a reviewer, responsible for reviewing up to 3 submissions.
We have a small number of free conference registrations, which we will offer to authors from historically underrepresented groups.
Important dates:
Submission deadline: October 4th, 2023
Acceptance notification: October 18th, 2023
Workshop: December 15th, 2023
Invited Speakers:
Reuth Mirsky, Bar Ilan University
Olexandr Isayev, Carnegie Mellon University
Susan Murphy, Harvard University
Yonatan Bisk, Carnegie Mellon University
Gianluca Baldassarre, Italian Institute of Cognitive Sciences and Technologies
Organizers: Ishan Durugkar (Sony AI), Ben Eysenbach (Princeton University), Jason Ma (University of Pennsylvania), Andi Peng (MIT), Tongzhou Wang (MIT), Amy Zhang (UT Austin)