12 citations · 30 across the 23 of their papers we have counts for
26 papers
Approximately Solving Continuous-Time Mean Field Games with Finite State Spaces
Yannick Eich, Christian Fabian, Kai Cui +1
Mean field games (MFGs) offer a powerful framework for modeling large-scale multi-agent systems. This paper addresses MFGs formulated in continuous time with discrete state spaces,…
Mean-Field Control on Sparse Graphs: From Local Limits to GNNs via Neighborhood Distributions
Tobias Schmidt, Kai Cui
Mean-field control (MFC) offers a scalable solution to the curse of dimensionality in multi-agent systems but traditionally hinges on the restrictive assumption of exchangeability…
Fluctuation induced network patterns in active matter with spatially correlated noise
Sebastian Fehlinger, Kai Cui, Arooj Sajjad +2
Fluctuations play a central role in many fields of physics, from quantum electrodynamics to statistical mechanics. In active matter physics, most models focus on thermal fluctuatio…
Learning Mean Field Control on Sparse Graphs
Christian Fabian, Kai Cui, Heinz Koeppl
Large agent networks are abundant in applications and nature and pose difficult challenges in the field of multi-agent reinforcement learning (MARL) due to their computational and…
Bounded Rationality Equilibrium Learning in Mean Field Games
Yannick Eich, Christian Fabian, Kai Cui +1
Mean field games (MFGs) tractably model behavior in large agent populations. The literature on learning MFG equilibria typically focuses on finding Nash equilibria (NE), which assu…
FPGA-Based Neural Thrust Controller for UAVs
Sharif Azem, David Scheunert, Mengguang Li +4
The advent of unmanned aerial vehicles (UAVs) has improved a variety of fields by providing a versatile, cost-effective and accessible platform for implementing state-of-the-art al…
A Modular Aerial System Based on Homogeneous Quadrotors with Fault-Tolerant Control
Mengguang Li, Kai Cui, Heinz Koeppl
The standard quadrotor is one of the most popular and widely used aerial vehicle of recent decades, offering great maneuverability with mechanical simplicity. However, the under-ac…
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
Christian Fabian, Kai Cui, Heinz Koeppl
Learning the behavior of large agent populations is an important task for numerous research areas. Although the field of multi-agent reinforcement learning (MARL) has made signific…
Collaborative Optimization of the Age of Information under Partial Observability
Anam Tahir, Kai Cui, Bastian Alt +2
The significance of the freshness of sensor and control data at the receiver side, often referred to as Age of Information (AoI), is fundamentally constrained by contention for lim…
Learning Discrete-Time Major-Minor Mean Field Games
Kai Cui, Gökçe Dayanıklı, Mathieu Laurière +3
Recent techniques based on Mean Field Games (MFGs) allow the scalable analysis of multi-player games with many similar, rational agents. However, standard MFGs remain limited to ho…
Sparse Mean Field Load Balancing in Large Localized Queueing Systems
Anam Tahir, Kai Cui, Heinz Koeppl
Scalable load balancing algorithms are of great interest in cloud networks and data centers, necessitating the use of tractable techniques to compute optimal load balancing policie…
Optimal Collaborative Transportation for Under-Capacitated Vehicle Routing Problems using Aerial Drone Swarms
Akash Kopparam Sreedhara, Deepesh Padala, Shashank Mahesh +3
Swarms of aerial drones have recently been considered for last-mile deliveries in urban logistics or automated construction. At the same time, collaborative transportation of paylo…
UAV Swarms for Joint Data Ferrying and Dynamic Cell Coverage via Optimal Transport Descent and Quadratic Assignment
Kai Cui, Lars Baumgärtner, Burak Yilmaz +6
Both data ferrying with disruption-tolerant networking (DTN) and mobile cellular base stations constitute important techniques for UAV-aided communication in situations of crises w…
Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior
Kai Cui, Sascha Hauck, Christian Fabian +1
Recent reinforcement learning (RL) methods have achieved success in various domains. However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observ…
Major-Minor Mean Field Multi-Agent Reinforcement Learning
Kai Cui, Christian Fabian, Anam Tahir +1
Multi-agent reinforcement learning (MARL) remains difficult to scale to many agents. Recent MARL using Mean Field Control (MFC) provides a tractable and rigorous approach to otherw…
Mean Field Games on Weighted and Directed Graphs via Colored Digraphons
Christian Fabian, Kai Cui, Heinz Koeppl
The field of multi-agent reinforcement learning (MARL) has made considerable progress towards controlling challenging multi-agent systems by employing various learning methods. Num…
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning
Kai Cui, Anam Tahir, Gizem Ekinci +4
The analysis and control of large-population systems is of great interest to diverse areas of research and engineering, ranging from epidemiology over robotic swarms to economics a…
Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control
Kai Cui, Mustafa Burak Yilmaz, Anam Tahir +2
The optimal offloading of tasks in heterogeneous edge-computing scenarios is of great practical interest, both in the selfish and fully cooperative setting. In practice, such syste…
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control
Kai Cui, Mengguang Li, Christian Fabian +1
In recent years, reinforcement learning and its multi-agent analogue have achieved great success in solving various complex control problems. However, multi-agent reinforcement lea…
Learning Sparse Graphon Mean Field Games
Christian Fabian, Kai Cui, Heinz Koeppl
Although the field of multi-agent reinforcement learning (MARL) has made considerable progress in the last years, solving systems with a large number of agents remains a hard chall…
Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems
Anam Tahir, Kai Cui, Heinz Koeppl
Recent years have seen a great increase in the capacity and parallel processing power of data centers and cloud services. To fully utilize the said distributed systems, optimal loa…
Hypergraphon Mean Field Games
Kai Cui, Wasiur R. KhudaBukhsh, Heinz Koeppl
We propose an approach to modelling large-scale multi-agent dynamical systems allowing interactions among more than just pairs of agents using the theory of mean field games and th…
Motif-based mean-field approximation of interacting particles on clustered networks
Kai Cui, Wasiur R. KhudaBukhsh, Heinz Koeppl
Interacting particles on graphs are routinely used to study magnetic behaviour in physics, disease spread in epidemiology, and opinion dynamics in social sciences. The literature o…
Learning Graphon Mean Field Games and Approximate Nash Equilibria
Kai Cui, Heinz Koeppl
Recent advances at the intersection of dense large graph limits and mean field games have begun to enable the scalable analysis of a broad class of dynamical sequential games with…