Reinforcement Learning Journal (RLJ)

The Reinforcement Learning Journal (RLJ) is an annual peer-reviewed publication focusing on the field of reinforcement learning. (RLJ) is complemented by the Reinforcement Learning Conference (RLC), an associated event where researchers can present and elaborate on their findings that are published in RLJ. It is important to note that RLJ is an independent, peer-reviewed journal that serves as a primary source of scholarly articles, with the associated meeting, RLC, enhancing the reach and impact of the research published within the journal.

RLJ upholds the principles of open access, providing free online access to all its publications under the Creative Commons Attribution (CC BY) license. Authors retain copyright over their work, thus promoting the free exchange of ideas within the scientific community. For more information, see the RLJ Publication Agreement.

Submissions to RLJ are accepted on an annual basis, adhering to a specific submission deadline each year. Authors interested in contributing are encouraged to review the detailed submission guidelines and deadlines in our Call for Papers (Note: The call for papers each year will be updated a few months prior to the deadline, and can be expected to fall around the same date each year.)

ISSN 2996-8577 (Online)

ISSN 2996-8569 (Print)

ISBN 979-8-218-41163-3

2024, Vol 1–5 DOI: 10.5281/zenodo.13899776

2024, Vol 1–5 XML: link

Editors

The editors of RLJ, who also serve as the Organizers and Program Committee for RLC are:

• Philip S. Thomas (Editor in Chief), Manning College of Information and Computer Sciences, University of Massachusetts.
• Feryal Behbahani, Google DeepMind.
• Glen Berseth, Department of Computer Science and Operations Research, Université de Montréal.
• Scott M. Jordan, Department of Computing Science, University of Alberta.
• Scott Niekum, Manning College of Information and Computer Sciences, University of Massachusetts.
• Andrew Patterson, Department of Computing Science, University of Alberta.
• Eugene Vinitsky, Department of Civil and Urban Engineering, New York University.
• Adam White, Department of Computing Science, University of Alberta.
• Martha White, Department of Computing Science, University of Alberta.
• Amy Zhang, Department of Electrical and Computer Engineering, University of Texas at Austin.