Summer 2022 at 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. Although, we originally hoped to continue the every-other-year pattern with a conference this summer, we’ve decided to postpone the meeting a year to simplify logistics. Stay tuned for updates as the conference gets closer.
Invited speakers include: 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)