Deployable RL: From Research to Practice


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


Announcements

  1. We've posted the call for papers! The paper submission deadline is May 17, 2024 (extended from May 8 for RLC notifications).
  2. We've posted a tentative schedule for the full-day event on August 9, 2024 in Amherst, MA.
  3. We've posted the list of accepted papers!


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.


Keynote Speakers

Hamsa Bastani

Hamsa Bastani
Wharton, University of Pennsylvania

Minmin Chen

Minmin Chen
Google Deepmind

Aviral Kumar

Aviral Kumar
Google DeepMind
CMU

Name 4

John Langford
Microsoft Research NYC

Name 1

Hongseok Namkoong
Columbia Business School

Panel

Minmin Chen

Minmin Chen
Google Deepmind

Omer Gottsman

Omer Gottsman
Amazon

Daniel Russo

Daniel Russo
Columbia Business School

Cathy Wu

Cathy Wu
MIT

Zheqing (Bill) Zhu

Zheqing (Bill) Zhu
Meta
Panel moderator

Schedule

The schedule below is tentative and subject to change.

AM                      
08:55 - 09:00 Opening remarks
09:00 - 09:40 Keynote: Minmin Chen
      Talk Title TBA
09:40 - 10:20 Keynote: Hongseok Namkoong
      Adaptive Experimentation at Scale
10:20 - 11:20 Morning poster session + coffee break
11:20 - 12:00 Keynote: John Langford
      Contextual Bandit Systems
PM                      
12:00 - 01:00 Lunch break (on your own)
01:00 - 02:00 Panel: Minmin Chen, Omer Gottsman, Daniel Russo, Kaushik Subramanian, Cathy Wu, Zheqing (Bill) Zhu
      Challenges Surrounding RL Deployment
02:00 - 02:40 Afternoon poster session + coffee break
02:40 - 03:20 Keynote: Hamsa Bastani
      Lessons from Deploying Algorithms for Public Health
03:20 - 04:00 Keynote: Aviral Kumar
      Converting Foundation Models to Deployable Agents via Offline and Online RL
04:00 - 04:05 Concluding remarks


Organizers

Aldo Pacchiano

Aldo Pacchiano
Boston University

Yi Wan

Yi Wan
Meta

Kelly W. Zhang

Kelly W. Zhang
Columbia Business School

Angela Zhou

Angela Zhou
Marshall School of Business, USC