About RLC
The first Reinforcement Learning Conference (RLC) will be held in Amherst, Massachusetts from August 9–12, 2024. RLC is an annual international conference focusing on reinforcement learning. RLC provides an archival venue where reinforcement learning researchers can interact and share their research in a more focused setting than typical large machine learning venues. The RLC peer review process prioritizes rigorous methodology over perceived importance, aiming to foster scholarly discussions on both well-established and emerging topics in RL.
We invite submissions presenting new and original research on topics including, but not limited to:
RL algorithms (e.g., new algorithms for existing settings and new settings)
Hierarchical RL (e.g., skill discovery, hierarchical representations and abstractions)
Exploration (e.g., intrinsic motivation, curiosity-driven learning, exploration-exploitation tradeoff)
Theoretical RL (e.g., complexity results, convergence analysis)
Social and economic aspects (e.g., safety, fairness, interpretability, privacy, trustworthiness, human-AI interaction, philosophy)
Bandit algorithms (e.g., theoretical contributions, practical algorithms)
Planning algorithms (e.g., decision-making under uncertainty, model-based approaches)
Foundations (e.g., showing relationships between methods, unifying theory, clarifying misconceptions in the literature)
Evaluation (e.g., methodology, meta studies, replicability, and validity)
Applied reinforcement learning (e.g., medical, operations, traffic)
Deep reinforcement learning (e.g., analysis on the interplay between RL and deep learning models)
Multi-agent RL (e.g., cooperative, competitive, self-play, etc)
Multidisciplinary work (RL research that relates to other fields)
RL Systems (e.g., distributed training, multi-GPU training)
RL from human feedback (e.g. reward learning from human data, human-in-the-loop learning, etc.)
We also welcome interdisciplinary research that does not fit neatly into existing categories, but which falls under the broad scope of reinforcement learning research.
Advisory Committee
Peter Stone (UT Austin)
Satinder Singh (University of Michigan)
Emma Brunskill (Stanford)
Michael Littman (Brown)
Shie Mannor (Technion, NVIDIA)
Michael Bowling (U Alberta)
Sergey Levine (UC Berkeley)
Balaraman Ravindran (IIT Madras)
Sham Kakade (Harvard)
Benjamin Rosman (University of the Witwatersrand)
Marc Deisenroth (UCL)
Andrew Barto (UMass Amherst)
