Full papers
Original research contributions with substantial technical depth and experimental validation.
Machine Learning/CORE Unranked
AutoRL@RLC 2026
RLC Workshop on Automated Reinforcement Learning
AutoRL@RLC 2026 is a workshop focused on automated reinforcement learning, bringing together researchers to present and discuss advances in automating components of RL systems such as reward design, architecture search, hyperparameter tuning, and policy learning. The workshop aims to foster collaboration and innovation in making RL more accessible and efficient through automation.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Preliminary results, work-in-progress, or position papers with concise presentation.
Journal-first papers
Papers that have been accepted or are under review at a journal, with a focus on extended and mature work.
Research areas in scope
01
Topics
Automated reward designAutomated architecture search for RLAutomated hyperparameter tuningAutomated feature engineeringAutomated curriculum learningAutomated exploration strategiesAutomated policy learning and representationMeta-learning for automated RLBenchmarking and evaluation of automated RL methodsApplications of automated RL in real-world domains
Policies worth checking twice
- Submissions must be anonymized for double-blind review.
- Full papers are limited to 8 pages, short papers to 4 pages, and journal-first papers to 12 pages, all including references.
- Dual submission to other conferences or journals is not allowed during the review period.
- Author lists and affiliations cannot be changed after the submission deadline.
- Authors must include a statement on the use of AI tools in the submission.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026