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.
We invite extended abstracts for contributed poster presentations and oral presentations.
We welcome submissions of original research related to “learning and decision making over time to achieve a goal”, coming from any discipline or disciplines, describing empirical results from human, animal or animat experiments, and/or theoretical work, simulations and modeling. Contributions should be aimed at an interdisciplinary audience, but not at the expense of technical excellence. This is an abstract-based meeting, with no published conference proceedings. As such, work that is intended for, or has been submitted to, other conferences or journals is also welcome, provided that the intent of communication to other disciplines is clear.
Submissions should consist of a summary (max 2000 characters; text only), and an extended abstract of between one and four pages (including figures and references). LaTeX and RTF templates, and sample submissions, are available here: https://rldm.org/submit/
Note: Only the summary will be made available in the (electronic) abstract booklets. The extended abstract will be used for reviewing, and will be available online only pending on authors’ separate explicit permission. Online availability will have no bearing on the review process and authors are encouraged to include new, unpublished, findings which they do not want to make publicly available.
To submit your abstract please go to https://cmt3.research.microsoft.com/RLDM2022
Submissions will be reviewed for relevance to the topic and for quality. Exceptional abstracts will be selected for oral presentations and for poster spotlight presentations.