The Berkeley–Hopkins Seminar Series on Learning-Enabled Multi-Agent Systems (LEMAS) is a weekly seminar that brings together researchers working at the forefront of learning and/or incentive design in multi-agent systems. We hope to create a platform for sharing recent advances, discussing key challenges, and fostering new research directions. We are based at Johns Hopkins University and UC Berkeley and currently operating in a hybrid format (talks occur both in-person and livestreamed over Zoom).
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Time: Every Friday, 10-11 a.m. Pacific Time
Locations (in-person):
UC Berkeley: Sutardja Dai Hall, 7th floor, Newton Room
Johns Hopkins University: Gilman 377, Baltimore, MD
Zoom link for the virtual attendance: https://wse.zoom.us/j/93974270620
Organizers:
Chinmay Maheshwari (Assistant Professor, ECE, Johns Hopkins University)
Shankar Sastry (Professor, EECS, UC Berkeley)
Pan-Yang Su (Ph.D. Student, EECS, UC Berkeley)
Fall 2025 Schedule
Fall 2025 Schedule
Note: Presentation titles in parentheses provide tentative descriptions of upcoming talks, and will be replaced with actual presentation titles when available. Titles for past talks link to more information about the talk and speaker, as well as a recording and/or slide deck when available.
Date Location Speaker Presentation Title
August 22 Virtual Maryam Kamgarpour Learning equilibria in games with bandit feedback
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August 29 No seminar
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September 5 Virtual Guannan Qu Locally Interdependent Multi-Agent MDP: A Scalable
Approach to Partially Observable Locally Dependent
Systems
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September 12 UCB Jorge Poveda Deception and Incentives in Multi-Agent Learning:
A Control-Theoretic View
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September 19 Virtual Florian Dörfler TBD
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September 26 JHU Rasoul Etesami TBD
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October 3 UCB Eric Mazumdar TBD
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October 10 TBD Thinh Doan TBD
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October 17 TBD TBD TBD
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October 24 TBD Amanda Prorok TBD
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October 31 TBD TBD TBD
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November 7 TBD Ketan Savla TBD
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November 14 TBD TBD TBD
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November 21 TBD Niao He TBD
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November 28 No speaker (holiday)
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December 5 TBD Rahul Mangharam TBD
TalK information (Fall 2025)
TalK information (Fall 2025)
Maryam Kamgarpour (EPFL, Switzerland)
Title: Learning equilibria in games with bandit feedback
Abstract: A significant challenge in managing large-scale engineering systems, such as energy and transportation networks, lies in enabling autonomous decision-making of interacting agents. Game theory offers a framework for modeling and analyzing this class of problems. In many practical applications, each player only has partial information about the cost functions and actions of others. Therefore, a decentralized learning approach is essential to devise optimal strategies for each player. My talk will focus on recent advances in decentralized learning in static and in Markov games under bandit feedback. It highlights challenges compared to single agent learning and presents algorithms with provable convergence. The first part will focus on learning in continuous action static games. The second part will explore Markov games, presenting our learning approaches for zero-sum Markov games and coarse-correlated equilibria in general-sum Markov games. I will conclude with presenting few open research directions.
Bio: Maryam Kamgarpour is a professor in the School of Engineering of École Polytechnique Fédérale de Lausanne. Prior to joining EPFL, she served as a faculty at the University of British Columbia and at ETH Zürich. Her research focuses on advancing fundamental understanding of multi-agent decision-making in uncertain and dynamic environments. Towards this goal, she develops algorithms for safe stochastic control and reinforcement learning, and game theory and mechanism design. Her theoretical research is driven by control challenges arising in intelligent transportation networks, robotics and power grid systems. She holds a Doctor of Philosophy in Engineering from the University of California, Berkeley and a Bachelor of Applied Science from University of Waterloo, Canada. She has received the NASA High Potential Individual Award and the NASA Excellence in Publication Award for her work on air traffic control with NASA Ames Research Center, and the European Union Starting Grant for her work on multi-agent decision making and distributed control for power systems. She is the recipient of the 2022 IEEE Transactions on Control of Network Systems Outstanding Paper Award and the 2024 European Control Award. She is a fellow of ELLIS and an associate editor of IEEE Transactions on Automatic Control.
Guannan Qu (CMU, United States)
Title: Locally Interdependent Multi-Agent MDP: A Scalable Approach to Partially Observable Locally Dependent Systems
Abstract: Partially observable multi-agent MDP problems are challenging to solve both because of the exponential growth in the state space and partial observability. These problems are often modelled using a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) which is NEXP-Complete and generally seen as intractable to solve. However, for many important partially observable problems such as cooperative navigation, obstacle avoidance, and formation control with applications in autonomous driving, UAVs, robot navigation etc., they satisfy a locality assumption of the interactions such as local collisions or local formation constructions.
In this presentation, we will introduce a new framework, Locally Interdependent Multi-Agent MDP (LIMDP), that leverages the above locality assumptions. Importantly, we show that LIMDP will have solutions that are more tractable to compute than general Dec-POMDP and can even counteract the curse-of-dimensionality. Further, these more tractable partially observable solutions are theoretically provable to be exponentially close to the fully observable joint optimal policy with respect to the visibility. This can be matched with a lower bound to establish near-optimality. We will present various simulations, and in particular demonstrate the scalability of these methods with a simulation of 100 agents navigating in a grid world without collision that was computed without the use of any empirical methods.
Bio: Guannan Qu has been an Assistant Professor at the Electrical and Computer Engineering Department of Carnegie Mellon University since September 2021. He received his B.S. degree in Electrical Engineering from Tsinghua University in Beijing, China in 2014, and his Ph.D. in Applied Mathematics from Harvard University in Cambridge, MA in 2019. He was a CMI and Resnick postdoctoral scholar in the Department of Computing and Mathematical Sciences at California Institute of Technology from 2019 to 2021. He is the recipient of NSF CAREER Award, Finalist of ICRA 2025 Best Conference Paper Award, Caltech Simoudis Discovery Award, PIMCO Fellowship, Amazon AI4Science Fellowship, and IEEE SmartGridComm Best Student Paper Reward. His research interest lies in control, optimization, and machine/reinforcement learning with applications to power systems, multi-agent systems, Internet of things, smart city, etc.
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