Reinforcement Learning Conference (RLC) 2024, August 9
Amherst, MA, United States
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Program:
Speakers | Panelists |
Schedule | Organization
Contact us: deployable.rl@gmail.com
Real-world applications pose distinct challenges for decision-making algorithms, especially during the deployment phase. These include high-dimensional observation and action spaces, tasks that may be partially observable or non-stationary, and feedback that is often unspecified, delayed, or corrupted. Furthermore, feedback rarely comes in the form of a scalar reward function, exploration is often prohibitively costly, and safety considerations are a prerequisite for trained models to be deployed.
Reinforcement learning (RL) and contextual bandit (CB) algorithms have been studied in many settings, including healthcare, recommender and advertising systems, resource allocation and operations management, hardware and engineering design, and more recently, large foundation models. Despite many studies focusing on applying RL to such domains, the majority of solutions are not eventually deployed. An important question for the RL community to ask is:
What pieces of the puzzle are we missing and what new methods are needed to push these ideas forward so that they become truly deployable?
This workshop aims to place a spotlight on this topic, with the goal of advancing RL and CB algorithms towards becoming a widespread industry standard. To this end, we invite contributions on theory and practice of RL aimed at facilitating deployment to real-world problems, examples of which include but are not limited to:
The above are only a handful of suitable topics. We welcome submissions on any topic that focuses on the RL deployment process. The submissions can be 4-8 pages in length and be of either a theoretical/methodological or empirical nature.
Hamsa Bastani
Wharton, University of Pennsylvania
Minmin Chen
Google Deepmind
Aviral Kumar
Google DeepMind
CMU
John Langford
Microsoft Research NYC
Hongseok Namkoong
Columbia Business School
Zheqing (Bill) Zhu
Meta
Moderator
Remi Cadane
Hugging Face
Dhruv Madeka
Amazon
Daniel Russo
Columbia Business School
Kaushik Subramanian
Sony AI
Cathy Wu
MIT
More panelists to be announced!
The schedule below is still tentative and subject to change.
AMÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | Â |
08:55 - 09:00 | Opening remarks |
09:00 - 09:35 | Keynote 1 + Q&A |
09:35 - 10:10 | Keynote 2 + Q&A |
10:10 - 11:25 | Morning poster session + coffee break |
11:25 - 12:00 | Keynote 3 + Q&A |
PMÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â | Â |
12:00 - 01:00 | Lunch break |
01:00 - 01:35 | Keynote 4 + Q&A |
01:35 - 02:10 | Keynote 5 + Q&A |
02:10 - 03:10 | Afternoon poster session + coffee break |
03:10 - 03:55 | Panel: Challenges surrounding RL deployment |
03:55 - 04:00 | Concluding remarks |
Meta
Yonathan Efroni
Meta
Mohammad Ghavamzadeh
Amazon
Daniel R. Jiang
Meta
Aldo Pacchiano
Boston University
Yi Wan
Meta
Kelly W. Zhang
Columbia Business School
Angela Zhou
Marshall School of Business, USC