Goal-Conditioned Reinforcement Learning Workshop

We are actively looking for reviewers for our workshop. If you are interested in joining our Program Committee as a reviewer, please fill out this form.

Motivation

Learning goal-directed behavior is one of the classical problems in AI, one that has received renewed interest in recent years and currently sits at the crossroads of many seemingly-disparate research threads: self-supervised learning , representation learning, probabilistic inference, metric learning, and duality.

Our workshop focuses on these goal-conditioned RL (GCRL) algorithms and their connections to different areas of machine learning. Goal-conditioned RL is exciting not just because of these theoretical connections with different fields, but also because it promises to lift some of the practical challenges with applying RL algorithms: users can specify desired outcomes with a single observation, rather than a mathematical reward function. As such, GCRL algorithms may be applied to problems varying from robotics to language models tuning to molecular design to instruction following.

Our workshop aims to bring together researchers studying the theory, methods, and applications of GCRL, researchers who might be well posed to answer questions such as:

How does goal-directed behavior in animals inform better GCRL algorithmic design?
How can GCRL enable more precise and customizable molecular generation?
Do GCRL algorithms provide an effective mechanism for causal reasoning?
When and how should GCRL algorithms be applied to precision medicine?

Goal

The workshop aims to foster an inclusive environment where researchers and practitioners from all backgrounds can engage in discussions and build collaborations on the theory, methods, and applications of GCR.

Broadly, the workshop will focus on the following topics and problems:

Connections: What are the connections between GCRL and representation learning, few-shot learning, and self-supervised learning? When does (say) effective representation learning emerge from GCRL?
Future directions: What are limitations of existing methods, benchmarks, and assumptions?
Algorithms: How might we improve existing methods, and do this in a way that enables applications to broader domains (e.g., molecular discovery, instruction-following robots)?

Speakers

Gianluca Baldassarre

Italian Institute of Cognitive Sciences and Technologies

Yonatan Bisk

Carnegie Mellon University

Olexandr Isayev

Carnegie Mellon University

Reuth Mirsky

Bar Ilan University

Susan Murphy

Harvard University

Panelists (TBA)

Schedule

Time (GMT-6)
09:00 am - 10:00 am		Poster Session 1
10:00 am - 10:40 am		Invited Speaker 1 TBD
10:45 am - 11:25 am		Invited Speaker 2 TBD
11:30 am - 12:10 pm		Invited Speaker 3 TBD
12:15 pm - 13:45 pm		Lunch Break
13:45 pm - 14:30 pm		Panel Discussion
14:30 pm - 15:10 pm		Invited Speaker 4 TBD
15:15 pm - 15:45 pm		Contributed Talks TBD
15:45 pm - 16:25 pm		Invited Speaker 5 TBD
16:30 pm - 17:30 pm		Poster Session 2

Call for Contributions
▻ 5-min video or 2-page report (You choose!)

Areas of Interest

We solicit submissions related to (but not limited to) the following topics:

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.
Examples might include representation learning, self-supervised learning, adversarial training, probabilistic inference, metric learning, duality, etc.
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

NOTE: While there isn't an RL-specific workshop at NeurIPS this year, we will reject papers that only focuses on RL but not GCRL. We encourage authors to take methods developed for other problem settings and try applying them to GCRL domains (e.g., online robotics benchmark, offline robotics benchmark, language-conditioned agents).

Accessible Submission Format

The workshop will accept submissions in a more casual format to encourage new ideas and to be more accessible.

A submission choose one of two tracks, which have different submission formats:
- Video Track: Submit a video (e.g., narrated slide deck, recorded talk) in mp4 format with at most 5 minutes.
  Video track submissions must provide a pdf appendix, containing sufficient details of the method and experiments. Reviewers are instructed to evaluated submission based on the video, and consult the appendix if details aren't clear.
  
  The appendix should be in the NeurIPS style. At most 2-pages of appendix can be details of methods and experiments.
- Short-Report Track: Submit A report in the NeurIPS style with at most 2 pages (unlimited on appendices).
Submissions should maximize clarity and explanation. E.g., concise clear bullet points are preferred (even with incomplete sentences) over long detailed paragraphs.
Submissions should still properly justify their significance.
Accepted submissions will be required to submit a 4-page (or shorter) report containing sufficient details for future reference and reproducibility.

Submission Instructions

All submissions will be managed through OpenReview.
The review process is double-blind so the submission should be anonymized. For video submission, natural voice narration is still considered anonymized. However, the video must not show authors' faces or names.
Format: 5-minute video or 2-page report. See above.
One author of each submission must serve 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.
All participants must adhere to the NeurIPS Code of Conduct.

Review Guideline

Submissions will be evaluated based on clarity, novelty, soundness, and relevance to theme of the workshop. Both empirical and theoretical contributions are welcomed.
Reviewers are instructed to give feedbacks and ideas for future improvements.
(To be finalized)