In recent years, many novel methods and directions have emerged in reinforcement learning and control. A particularly exciting development is the use of online optimization and statistical learning techniques in control theory. This has led to novel methods and guarantees in various contexts, including in stochastic and adversarial environments, system identification, iterative planning and sequence prediction. Other topics we will cover include new connections between control and both model-free and model-based reinforcement learning, as well as learning dynamical systems. We aim to bring together researchers to facilitate progress along these lines of investigation, and discuss important future directions in reinforcement learning, control, learning dynamical systems and applications to sequence prediction.
Poster Session and Lightning Talks
This workshop will include a poster sessionand lightning talks for early career researchers (including graduate students). In order to propose a poster or a lightning talk, you must first register for the workshop, and then submit a proposal using the form that will become available on this page after you register. You can request to do one, or both. The registration form should not be used to propose a posteror a lightning talk.
The deadline for proposing is Sunday, March 15, 2026. If your proposal is accepted, you should plan to attend the event in-person.
Max Raginski
University of Illinois Urbana-Champaign (UIUC)
D
R
Daniel Russo
Columbia University
D
S
Dale Schuurmans
University of Alberta and Google DeepMind
M
S
Max Simchowitz
Carnegie Mellon University
S
T
Stephen Tu
University of Southern California
V
T
Vasileios Tzoumas
University of Michigan
B
V
R
Ben Van Roy
Stanford University
Registration
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Contact [email protected] to request
disability-related accommodations.
In order to register for this workshop, you must have an IMSI account and be logged in.