Reinforcement Learning Bootcamp

Description

As part of IMSI’s long-program on Theoretical Advances in Reinforcement Learning and Control, the Reinforcement Learning Bootcamp is designed as an intensive, foundational tutorial series. Its goal is to equip participants with the core theoretical tools and conceptual framework needed to fully engage with the subsequent workshops in the program.

This bootcamp is especially targeted at early-career researchers, graduate students, postdocs, and others who plan to contribute to the frontier of RL theory or control theory. Participants will benefit most if they come with basic familiarity in probability, linear algebra, optimization, and basic machine learning, although the tutorials aim to bring everyone up to speed.

In-Person Registration

Seats are limited at the venue, which means that in-person registration may be capped prior to the workshop start date. If capacity is reached, a waitlist will be imposed, which the registration form will reflect. Early registration is strongly encouraged.

All in-person registrants must wait to receive an invitation to attend in-person from IMSI before traveling, which generally begin to be sent out 4-6 weeks in advance.

All registrants (online and in-person) will receive zoom links and are welcome to attend online.

Schedule