Foundations of Multi-Agent and Mean Field Reinforcement Learning

Description

Reinforcement learning (RL) is widely regarded as a promising frontier in artificial intelligence and a significant advancement in machine learning. This view is supported by the highly publicized successes of deep reinforcement learning in domains such as video games and classical strategy games like chess and Go. However, many of these successes rely on heuristic-based algorithmic implementations. While these heuristics often perform remarkably well, their effectiveness and underlying mechanisms remain only partially understood, even by their developers. Moreover, the majority of current RL applications focus on robotic systems, including self-driving cars, and primarily address single-agent control problems. Recently, there has been growing interest in multi-agent reinforcement learning, which involves coordinating systems such as fleets of autonomous vehicles, presenting heightened mathematical and computational challenges. These challenges become even more pronounced in systems involving a large number of agents, where direct modeling and control are computationally prohibitive. In such cases, mean-field approximations have emerged as a powerful tool to scale reinforcement learning algorithms by approximating the interactions among agents through aggregate effects. This workshop will focus on multi-agent RL and mean-field RL. Besides the talks, we will organize one or two tutorials on Markov Decision Processes and Mean Field Games to help students be more familiar with the topic of the workshop. We will also organize a special lecture and a session of lightning talks for the long program attendees.

Lightning Talks

This workshop will include lightning talks for early career researchers (including graduate students). In order to propose 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. The registration form should not be used to propose a lightning talk.

The deadline for proposing is Sunday, March 22, 2026. If your proposal is accepted, you should plan to attend the event in-person.

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.

Registration

IMSI is committed to making all of our programs and events inclusive and accessible. Contact [email protected] to request disability-related accommodations.