The 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM2022)

June 8-11, 2022

Brown University, Providence, RI, USA

Over the last few decades, reinforcement learning and decision making have been the focus of an incredible wealth of research spanning a wide variety of fields including psychology, artificial intelligence, machine learning, operations research, control theory, animal and human neuroscience, economics and ethology. Key to many developments in the field has been interdisciplinary sharing of ideas and findings. The goal of RLDM is to provide a platform for communication among all researchers interested in “learning and decision making over time to achieve a goal”. The meeting is characterized by the multidisciplinarity of the presenters and attendees, with cross-disciplinary conversations and teaching and learning being central objectives along with the dissemination of novel theoretical and experimental results. The main meeting will be single-track, consisting of a mixture of invited and contributed talks, tutorials, and poster sessions.

Stay tuned for updates as the conference gets closer.

CONFIRMED SPEAKERS:

Josh Tenenbaum (MIT)
Yunzhe Liu (UCL)
Jill O’Reilly (Oxford)
Nao Uchida (Harvard)
Melissa Sharpe (UCLA)
Alexandra Rosati (Michigan)
Frederike Petzschner (Brown)
Oriel Feldman-Hall (Brown)
Scott Niekum (UT Austin)
Satinder Singh Baveja (Michigan and DeepMind)
Stephanie Tellex (Brown)
Martha White (Alberta)
Sonia Chernova (Georgia Tech)
Jeannette Bohg (Stanford)
Jakob Foerster (Facebook AI Research)

RLDM2022 ORGANIZERS:

GENERAL CHAIRS
Catherine Hartley
Michael Littman

PROGRAM CHAIRS
Roshan Cools
Peter Stone

LOCAL CHAIRS
Michael Frank
George Konidaris

EXECUTIVE COMMITTEE
Yael Niv
Peter Dayan
Satinder Singh
Rich Sutton
Emma Brunskill
Ross Otto

RLDM 2019

CONGRATULATIONS TO THE WINNERS OF THE ‘BEST PAPER’ AWARD:

Hyperbolic Discounting and Learning over Multiple Horizons
Liam Fedus, Carles Gelada, Yoshua Bengio, Marc G. Bellemare
Hugo Larochelle

A distributional code for value in dopamine-based reinforcement learning
Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather
Demis Hassabis, Remi Munos, Matthew Botvinick

 

Schedule at a Glance

Program Brochure

Conference Sponsors

Talk and Poster Abstracts

Reinforcement learning and decision making have been the focus of research spanning a wide variety of fields including psychology, artificial intelligence, machine learning, operations research, control theory, animal and human neuroscience, economics, and ethology. Key to many developments in the field has been interdisciplinary sharing of ideas and findings. RLDM is the only conference that brings all these communities together.

The focus of the conference can be broadly construed as decision making over time to achieve a goal. Our aim is to create a recurring meeting characterized by the multidisciplinarity of the presenters and attendees, with cross-disciplinary conversations and teaching and learning being central objectives along with the dissemination of novel theoretical and experimental results.

To ensure that you receive future announcements about RLDM please join our mailing list at http://tinyurl.com/RLDMlist (you must log in to google to see the “join list” button, and choose “all email” from the options at the bottom).